CARGA DE LIBRERIAS

library(gplots)
library(foreign)
library(car)
library(plotly)
library(ggpubr)
library(ggplot2)
library(reprex)
library(dplyr)
library(readxl)
library(readr)
library(rpart)
library(glmulti)
library(caret)
library(InformationValue)
library(rvest)
library(stringi)
library(stringr)
library(dplyr)
library(fmsb)
library(dlookr)
library(ggrepel)
library(rattle)
library(corrplot)
library(factoextra)
library(mclust)
library(FactoMineR)
library(tidyr)
library(GGally)
library(gridExtra)
library(grid)
library(FSelector)
library(mlbench)
library(RRF)
library(wsrf)
library(Boruta)
options(warn=-1)

options(repr.plot.width = 18, repr.plot.height = 10)

IMPORTACION Y LECTURA DE LOS DATOS

options(repr.matrix.max.rows=600, repr.matrix.max.cols=200)

datos <- read_excel("C:/Users/34625/Downloads/PROYECTOS DE TRABAJO/Yankel Carolina Sena/fwddatayankelsena/original_yankel.xlsx",sheet='Hoja12')
datos

A tibble: 59 × 34

STATUS	CYCLES_BETWEEN_PET1_PET2	GENDER	AGE	TNM_STAGE	DIFF_WBC	DIFF_RBC	DIFF_HB	DIFF_PLT	DIFF_CRP	DIFF_ALBUMIN	DIFF_LDH	DIFF_eGFR	DIFF_AST	DIFF_ALT	DIFF_K	DIFF_BGL	BMI	DIFF_BW	DIFF_SPLEEN_UPTAKE	DIFF_BM_UPTAKE	DIFF_LIVER_UPTAKE	DIFF_ESTIMATED_SPLEEN_VOL	DIAGNOSTIC	TREATMENT	ECOGPS	COMORBIDITIES	CTCNCI	ACTION_TAKEN_	TIME_BETWEE_PET	dias	DIFF_SLR	DIFF_BMLR	OVERALL_TIME
<chr>	<dbl>	<chr>	<dbl>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
ALIVE	3	MALE	54	III	-1.83	-0.64	-2.20	-82	-0.56	0.98	-271.0	23.7	0.30	13.20	0.10	12	22.90	-4.00	0.02	0.12	0.44	-80.9	GINECOLOGICAL	ICI	SYMPTOMATIC BUT AMBULATORY	HYPERLIPIDEMIA	SEVERE	DRUG INTERRUPTED	2	25	0.029335236	0.109059370	2
ALIVE	2	FEMALE	79	IV	-3.08	0.14	0.70	-159	-3.80	1.33	22.0	-0.2	4.40	5.30	0.58	-14	16.47	0.30	-0.20	-0.60	-0.30	6.2	HEAD AND NECK	ICI	ASYMTOMATIC	HYPERTENSION	NO SIDE EFFECTS	DRUG INTERRUPTED	4	0	-0.156566790	-0.046889612	50
ALIVE	3	MALE	60	III	-3.18	-0.50	3.54	22	-0.06	-0.20	64.2	4.6	-2.60	1.10	0.33	12	20.15	-8.10	-0.12	0.34	0.27	9.1	LUNG CANCER	ICI	SYMPTOMATIC,<50% IN BED DURING THE DAY	DIABETES MELLITUS	MODERATE	DOSE REDUCED	4	14	-0.044698028	-0.365688558	2
ALIVE	3	MALE	76	II	-1.28	0.79	1.70	9	0.04	-0.12	-55.0	-8.9	-0.70	-6.70	0.05	-4	22.83	0.00	0.46	-0.40	0.59	-38.8	RENAL CANCER	ICI	ASYMTOMATIC	HYPERTENSION	NO SIDE EFFECTS	DOSE NOT CHANGED	6	0	-0.002886671	0.130612972	12
ALIVE	5	FEMALE	70	II	0.00	-0.09	-0.40	-95	-7.23	0.05	6.0	-13.2	2.70	2.20	0.06	-8	18.29	-0.10	-0.34	-0.80	-0.48	-87.6	MELANOMA	ICI	SYMPTOMATIC BUT AMBULATORY	DIABETES MELLITUS	NO SIDE EFFECTS	DOSE NOT CHANGED	6	0	-0.095215760	-0.252680247	12
ALIVE	4	MALE	54	IV	0.99	0.10	0.00	-3	0.02	-0.16	-4.0	-13.1	0.60	2.80	-0.47	-3	27.18	-10.80	0.00	-0.10	-0.53	34.4	LUNG CANCER	ICI	ASYMTOMATIC	CANCER RRECURRENCE	HIGHT SEVERE	DOSE NOT CHANGED	6	363	-0.167591661	0.072513788	18
ALIVE	2	MALE	60	III	-1.48	-1.23	-1.90	-60	0.02	-0.46	-16.0	14.1	5.20	0.60	-0.26	-12	22.76	1.40	-0.07	0.11	-0.17	-85.0	LUNG CANCER	ICI	ASYMTOMATIC	CHRONIC INFLAMMATION	NO SIDE EFFECTS	DOSE NOT CHANGED	9	0	0.114290017	0.200158109	7
ALIVE	5	MALE	69	III	-1.65	0.04	0.60	-354	4.66	0.52	-333.0	-9.2	-7.30	-23.90	-0.02	-70	17.36	-2.20	0.61	-0.18	0.61	11.3	HEAD AND NECK	ICI	SYMPTOMATIC,<50% IN BED DURING THE DAY	DIABETES MELLITUS	MODERATE	DOSE NOT CHANGED	9	95	0.223201621	0.314140976	16
ALIVE	6	MALE	64	IV	3.89	-0.78	-3.20	242	15.21	-1.22	-28.0	9.0	-10.40	-13.20	-0.23	70	23.65	-11.10	0.30	0.50	0.03	8.1	HEAD AND NECK	ICI	SYMPTOMATIC BUT AMBULATORY	HYPERLIPIDEMIA	SLIGTHLY SIDE EFFCTS	DOSE NOT CHANGED	10	28	0.163665694	0.070517745	65
ALIVE	12	MALE	72	III	-0.80	-0.37	-0.90	-26	-0.02	-0.09	-13.0	-2.1	0.00	-1.90	0.26	64	22.84	-6.60	0.24	0.48	0.65	-51.4	HEAD AND NECK	ICI	ASYMTOMATIC	CHRONIC INFLAMMATION	NO SIDE EFFECTS	DOSE NOT CHANGED	16	0	-0.117647059	0.083556150	1
ALIVE	29	MALE	71	III	-0.91	0.07	0.20	11	0.18	0.18	52.0	-8.7	-0.60	-0.10	0.59	11	21.34	0.00	-0.05	0.69	0.09	-46.1	HEAD AND NECK	ICI	ASYMTOMATIC	HYPERTENSION	NO SIDE EFFECTS	ADDED OTHER TREATMEN	21	0	0.312678365	0.619776214	41
ALIVE	6	MALE	67	III	-2.67	-0.86	2.30	-4	-0.36	-0.72	69.0	16.9	5.00	3.80	0.05	11	20.29	-1.10	0.20	0.70	0.52	66.9	LUNG CANCER	ICI	ASYMTOMATIC	CANCER RRECURRENCE	NO SIDE EFFECTS	DOSE NOT CHANGED	25	0	-0.160987704	-0.061448788	11
ALIVE	48	MALE	60	III	4.52	0.11	-1.90	-63	17.83	-0.61	-15.0	-24.1	-4.10	-4.40	-0.67	64	29.11	-6.20	1.13	2.08	0.31	177.0	RENAL CANCER	ICI	ASYMTOMATIC	HYPERLIPIDEMIA	SLIGTHLY SIDE EFFCTS	UNKNOW	25	735	0.413640470	-0.037127371	11
ALIVE	20	MALE	61	IV	1.71	0.89	3.70	-183	-1.24	1.74	86.0	-9.4	-0.50	1.80	-0.17	-11	20.12	0.00	0.15	-0.22	0.57	-44.5	MELANOMA	ICI	SYMPTOMATIC,<50% IN BED DURING THE DAY	DIABETES MELLITUS	NO SIDE EFFECTS	UNKNOW	38	0	0.125985126	0.396964147	15
DEATH	2	MALE	83	II	2.37	-0.12	1.60	44	0.00	0.00	0.0	0.0	0.00	0.00	0.00	14	24.46	-1.50	0.10	0.35	-0.03	31.6	MELANOMA	ICI	ASYMTOMATIC	CANCER RRECURRENCE	NO SIDE EFFECTS	DOSE NOT CHANGED	11	0	0.253714576	0.288722186	4
DEATH	3	MALE	68	I	3.22	-1.11	0.90	-59	3.02	-0.09	5.0	-19.6	-11.60	-7.50	0.17	6	24.41	-4.30	-0.05	-0.45	0.27	-2.1	MELANOMA	ICI	SYMPTOMATIC,<50% IN BED DURING THE DAY	CANCER RRECURRENCE	NO SIDE EFFECTS	DOSE NOT CHANGED	2	0	0.207638889	0.081944444	13
DEATH	18	MALE	77	II	0.37	0.63	1.90	-24	0.22	-0.06	46.0	7.5	6.50	4.10	0.48	-12	18.07	0.00	-0.13	0.20	-0.14	-2.4	RENAL CANCER	ICI	ASYMTOMATIC	CHRONIC INFLAMMATION	SEVERE	ADDED STEROIDS	10	3	-0.117647059	0.083556150	12
DEATH	4	MALE	60	III	-1.23	-0.28	-1.20	-11	2.69	0.10	194.0	-10.2	10.30	7.50	-0.01	1	23.14	4.00	-0.42	-0.40	-0.74	55.2	LUNG CANCER	ICI	SYMPTOMATIC BUT AMBULATORY	CHRONIC INFLAMMATION	MODERATE	UNKNOW	15	441	0.030665281	0.133264033	29
DEATH	7	MALE	64	IV	0.46	-0.41	-1.50	34	0.31	-0.44	-8.0	-4.6	-1.10	0.40	0.44	-6	22.12	5.70	0.27	0.46	-0.37	15.1	HEAD AND NECK	ICI	SYMPTOMATIC,<50% IN BED DURING THE DAY	DIABETES MELLITUS	MODERATE	DRUG INTERRUPTED	4	52	0.135997522	0.200123916	42
DEATH	15	MALE	67	II	-4.64	0.71	2.40	-189	-15.88	1.57	-510.0	-24.5	-2.90	-16.90	0.00	-27	14.08	44.02	-0.40	-0.19	0.19	2.4	HEAD AND NECK	ICI	SYMPTOMATIC BUT AMBULATORY	DIABETES MELLITUS	SLIGTHLY SIDE EFFCTS	DRUG INTERRUPTED	13	364	-0.513598150	-0.140500210	21
DEATH	5	MALE	83	IV	1.13	-0.21	-0.60	16	0.32	0.08	-9.0	-2.8	0.70	0.40	0.59	16	20.07	5.90	0.22	-0.21	0.34	-85.5	HEAD AND NECK	ICI	SYMPTOMATIC,<50% IN BED DURING THE DAY	HYPERTENSION	NO SIDE EFFECTS	DOSE NOT CHANGED	8	0	-0.193627043	-0.093738849	21
DEATH	4	FEMALE	56	II	7.74	-0.33	-0.80	463	-2.03	2.83	-140.0	45.3	-219.60	-250.80	-0.90	-18	21.79	-2.30	-0.16	0.26	-0.84	34.0	MELANOMA	ICI	SYMPTOMATIC BUT AMBULATORY	HYPERLIPIDEMIA	HIGHT SEVERE	DOSE REDUCED	9	233	-0.045342594	0.034875129	8
ALIVE	7	MALE	65	II	-1.68	-0.68	1.00	0	7.55	-0.13	8.0	-3.3	-6.90	-5.70	0.04	14	18.18	12.20	0.31	-0.54	-0.85	22.3	PANCREAS CANCER	CHEMO	ASYMTOMATIC	DIABETES MELLITUS	NO SIDE EFFECTS	UNKNOW	1	0	0.004147110	0.162187359	27
ALIVE	5	FEMALE	55	IV	-3.77	-1.93	1.40	-58	0.12	0.01	-29.0	6.7	-3.10	-10.20	0.48	25	25.60	4.60	0.40	0.59	-0.24	29.9	GINECOLOGICAL	CHEMO	SYMPTOMATIC,<50% IN BED DURING THE DAY	HYPERLIPIDEMIA	SEVERE	DRUG INTERRUPTED	2	20	-0.230769231	-0.330769231	10
ALIVE	2	FEMALE	75	III	-2.17	-0.94	-2.30	-100	0.00	0.27	21.0	-10.4	1.80	-2.40	-0.20	8	25.96	-5.40	0.19	0.65	0.88	-1.0	LUNG CANCER	CHEMO	SYMPTOMATIC BUT AMBULATORY	HYPERTENSION	MODERATE	DOSE REDUCED	2	15	0.071091909	0.511586803	41
ALIVE	6	MALE	74	III	-3.24	-1.16	-0.20	100	-0.46	-0.56	41.0	3.0	4.40	0.80	0.68	9	20.85	9.30	0.00	-0.20	0.60	19.0	PANCREAS CANCER	CHEMO	ASYMTOMATIC	HYPERTENSION	MODERATE	DOSE REDUCED	3	35	0.509097867	0.149792993	3
ALIVE	3	FEMALE	76	IV	-0.04	-0.43	-0.80	95	0.02	-0.10	103.0	-2.2	7.90	0.10	-0.33	2	20.90	-2.40	-0.12	0.84	-0.27	19.7	LUNG CANCER	CHEMO	ASYMTOMATIC	DIABETES MELLITUS	NO SIDE EFFECTS	DOSE NOT CHANGED	3	0	0.123854660	0.028606149	32
ALIVE	3	MALE	77	III	-5.70	-1.29	-2.60	-229	0.18	-0.21	45.0	-9.7	-19.70	-84.70	-0.10	-13	22.94	0.00	0.03	-0.33	0.84	98.8	LUNG CANCER	CHEMO	ASYMTOMATIC	HYPERTENSION	HIGHT SEVERE	DOSE REDUCED	3	6	0.047663153	-0.219056440	15
ALIVE	4	MALE	68	II	-0.47	0.05	5.80	69	-2.74	1.18	67.0	-16.2	1.20	-0.70	0.76	-20	21.57	5.40	0.26	0.55	0.46	4.1	LUNG CANCER	CHEMO	ASYMTOMATIC	HYPERTENSION	NO SIDE EFFECTS	DOSE NOT CHANGED	4	0	-0.026468254	0.043273810	14
ALIVE	2	MALE	68	II	-2.47	-0.73	-1.00	-61	-0.10	-0.12	25.0	13.4	-0.70	-2.10	0.19	-13	35.81	-1.30	-0.05	0.43	-0.07	15.1	LUNG CANCER	CHEMO	SYMPTOMATIC BUT AMBULATORY	DIABETES MELLITUS	NO SIDE EFFECTS	DOSE NOT CHANGED	4	0	-0.318764676	-0.090749584	15
ALIVE	3	FEMALE	86	IV	-4.78	-1.30	-3.50	-17	-6.38	-0.12	-2.0	-3.0	-15.20	-19.30	0.23	-8	26.75	-2.40	0.30	0.19	0.35	41.0	UROTHELIAL CARCINOMA	CHEMO	SYMPTOMATIC,<50% IN BED DURING THE DAY	HYPERTENSION	SEVERE	DOSE REDUCED	5	12	0.243005526	0.290715505	24
ALIVE	2	MALE	74	II	1.13	-0.30	-1.70	-32	0.27	-0.25	-12.0	1.2	-5.60	-5.60	-0.07	21	19.62	15.60	0.20	-0.30	-0.10	56.3	PANCREAS CANCER	CHEMO	SYMPTOMATIC,<50% IN BED DURING THE DAY	HYPERLIPIDEMIA	SEVERE	DOSE REDUCED	5	0	-0.005084746	-0.015906128	6
ALIVE	3	FEMALE	52	I	3.14	0.03	0.40	-9	0.07	0.05	91.0	8.9	4.30	16.80	0.09	9	22.19	-4.80	-0.37	0.77	-0.08	42.4	EWING SARCOMA	CHEMO	ASYMTOMATIC	DIABETES MELLITUS	NO SIDE EFFECTS	DOSE NOT CHANGED	5	0	0.087355212	-0.170045045	32
ALIVE	4	FEMALE	74	IV	2.19	0.44	1.30	59	-0.01	-0.24	40.0	1.9	-3.20	-8.60	0.14	24	17.78	5.60	-0.16	-0.01	-0.23	-1.8	HEAD AND NECK	CHEMO	SYMPTOMATIC BUT AMBULATORY	HYPERTENSION	MODERATE	DOSE NOT CHANGED	6	1	0.105820106	-0.078042328	23
ALIVE	13	FEMALE	70	IV	-1.92	-0.90	-1.60	-233	-1.94	1.15	-294.0	-3.3	-18.70	-6.40	-0.19	20	24.33	-1.20	0.08	-1.01	0.22	-55.6	GINECOLOGICAL	CHEMO	SYMPTOMATIC,<50% IN BED DURING THE DAY	HYPERLIPIDEMIA	NO SIDE EFFECTS	UNKNOW	7	0	0.083963069	0.159957950	15
ALIVE	2	MALE	62	III	1.53	-0.61	-0.40	-19	-0.05	-0.21	-5.0	-12.8	0.10	-4.20	0.88	19	16.24	0.80	-0.08	0.08	0.01	5.8	LUNG CANCER	CHEMO	ASYMTOMATIC	CHRONIC INFLAMMATION	NO SIDE EFFECTS	DOSE NOT CHANGED	8	0	-0.244661571	-0.313686865	63
ALIVE	3	MALE	79	IV	4.23	0.38	1.20	170	0.59	-0.50	-66.0	3.3	-111.50	-18.30	0.24	-118	20.64	8.90	-0.66	-0.76	-1.59	-9.1	RENAL CANCER	CHEMO	ASYMTOMATIC	CHRONIC INFLAMMATION	NO SIDE EFFECTS	DOSE NOT CHANGED	9	0	0.202321083	0.496711799	9
ALIVE	14	FEMALE	69	II	3.39	-1.31	-2.20	-59	-0.09	-0.60	16.0	34.3	-4.70	-13.80	0.10	-41	18.57	0.80	-0.39	0.06	-0.70	99.4	PANCREAS CANCER	CHEMO	SYMPTOMATIC >50 % IN THE BED	ATEROESCLEROSIS	NO SIDE EFFECTS	DOSE REDUCED	9	0	0.061267406	-0.157317420	4
ALIVE	10	FEMALE	65	II	-2.68	0.18	1.80	316	4.78	-1.70	14.0	47.7	4.70	-1.20	-0.56	15	16.51	15.10	0.22	-0.36	0.02	46.9	GINECOLOGICAL	CHEMO	SYMPTOMATIC,<50% IN BED DURING THE DAY	HYPERLIPIDEMIA	SEVERE	DOSE REDUCED	10	32	-0.054315003	-0.256317546	21
ALIVE	4	MALE	74	III	1.51	0.83	2.60	-110	-0.01	0.22	-81.0	-30.1	-11.50	-9.90	0.14	-7	22.08	-1.00	0.09	-0.12	0.18	-18.1	LUNG CANCER	CHEMO	ASYMTOMATIC	HYPERTENSION	MODERATE	DOSE NOT CHANGED	12	36	-0.159000071	-0.136205070	3
ALIVE	12	MALE	76	III	-0.49	-0.11	-0.40	-56	0.04	-0.01	31.0	-13.4	7.20	5.20	0.36	-56	26.80	-2.20	0.24	0.44	0.63	-110.2	LUNG CANCER	CHEMO	SYMPTOMATIC BUT AMBULATORY	ATEROESCLEROSIS	SEVERE	ADDED OTHER TREATMEN	14	51	-0.040974878	0.214845830	6
ALIVE	4	FEMALE	72	III	2.00	0.84	2.40	-16	-0.01	0.22	1.0	1.4	-6.70	-4.20	0.06	2	15.43	4.50	-0.80	0.90	0.14	18.0	EWING SARCOMA	CHEMO	ASYMTOMATIC	CANCER RRECURRENCE	NO SIDE EFFECTS	ADDED OTHER TREATMEN	17	0	-0.292250233	-0.619234360	20
ALIVE	14	FEMALE	76	III	3.86	1.15	3.80	15	-3.88	-1.50	30.0	-7.6	18.90	18.30	-0.10	-23	29.56	3.60	0.66	0.00	0.50	26.7	GINECOLOGICAL	CHEMO	SYMPTOMATIC,<50% IN BED DURING THE DAY	HYPERTENSION	NO SIDE EFFECTS	UNKNOW	19	0	-0.314585877	-0.182208995	20
ALIVE	4	FEMALE	56	III	-3.11	-0.22	-0.50	4	-0.10	-0.08	9.0	-20.0	-2.90	-1.60	-0.03	43	18.03	-1.30	-0.63	0.21	0.49	2.6	LUNG CANCER	CHEMO	SYMPTOMATIC BUT AMBULATORY	DIABETES MELLITUS	NO SIDE EFFECTS	UNKNOW	20	0	0.027777778	-0.125000000	1
ALIVE	4	MALE	70	III	-0.19	-0.44	-0.70	-17	-0.27	0.13	4.0	-5.7	-7.60	-12.00	0.36	-160	23.75	-5.60	-0.55	-1.20	0.10	31.0	LUNG CANCER	CHEMO	SYMPTOMATIC,<50% IN BED DURING THE DAY	CHRONIC INFLAMMATION	NO SIDE EFFECTS	DOSE NOT CHANGED	20	0	-0.095525727	-0.514605305	3
ALIVE	4	FEMALE	75	III	-2.30	0.14	0.40	-82	0.02	-0.31	16.0	-25.1	-6.10	-9.40	0.73	0	25.78	-4.90	0.10	0.20	-0.29	2.4	GINECOLOGICAL	CHEMO	ASYMTOMATIC	HYPERTENSION	SEVERE	DOSE REDUCED	33	312	0.048263534	0.141343207	5
DEATH	9	MALE	81	II	0.10	-0.19	-1.10	1	1.19	-0.06	-4.0	-2.2	-5.50	-8.80	1.07	16	16.17	0.00	0.34	-0.82	-0.65	-68.5	GASTRIC CANCER	CHEMO	ASYMTOMATIC	CANCER RRECURRENCE	MODERATE	DRUG INTERRUPTED	9	86	0.226870727	0.438938719	22
DEATH	9	MALE	73	III	-1.94	0.13	0.40	75	-1.67	-2.28	32.0	-32.7	15.60	-2.40	0.51	-1	16.90	26.20	0.20	0.70	0.52	-3.1	PANCREAS CANCER	CHEMO	SYMPTOMATIC BUT AMBULATORY	HYPERLIPIDEMIA	SEVERE	DRUG INTERRUPTED	4	195	0.065394796	0.097268444	16
DEATH	5	MALE	77	I	-2.19	-1.97	-5.20	-151	0.39	0.12	34.0	3.8	11.90	19.40	-0.64	30	25.21	5.30	-0.14	-0.72	0.30	-7.6	LUNG CANCER	CHEMO	ASYMTOMATIC	CHRONIC INFLAMMATION	NO SIDE EFFECTS	DRUG WIHDRAWN	5	0	-0.080303030	-0.620707071	9
DEATH	6	MALE	74	I	5.48	-0.77	-2.80	15	7.21	-0.37	472.0	6.8	-1.10	-2.60	0.35	29	23.51	7.50	0.34	0.01	-0.32	-5.6	GASTRIC CANCER	CHEMO	ASYMTOMATIC	CANCER RRECURRENCE	NO SIDE EFFECTS	DOSE NOT CHANGED	12	0	-0.022968112	-0.097195217	1
DEATH	6	MALE	70	I	0.38	0.59	0.20	-9	0.25	-0.05	18.0	-6.3	-0.80	-8.60	0.63	17	16.68	18.70	-0.07	0.09	-0.01	33.1	LUNG CANCER	CHEMO	SYMPTOMATIC BUT AMBULATORY	HYPERLIPIDEMIA	SEVERE	DOSE REDUCED	12	50	-0.136822783	-0.321107069	13
DEATH	6	MALE	63	II	2.42	0.49	4.00	26	2.96	-1.35	170.0	9.9	-55.90	-84.90	-0.34	-70	17.89	24.10	-0.06	0.21	-0.25	84.2	PANCREAS CANCER	CHEMO	SYMPTOMATIC,<50% IN BED DURING THE DAY	DIABETES MELLITUS	NO SIDE EFFECTS	DOSE REDUCED	7	0	-0.195751634	-0.070728291	64
DEATH	5	FEMALE	61	II	-0.96	-1.44	-2.70	-12	0.03	-0.12	15.0	-0.9	6.60	14.30	0.91	29	20.44	6.90	-0.41	0.24	0.51	34.2	GINECOLOGICAL	CHEMO	ASYMTOMATIC	HYPERLIPIDEMIA	HIGHT SEVERE	DOSE REDUCED	11	114	-0.101647059	0.002627451	12
DEATH	2	MALE	79	III	-2.67	-0.86	-3.50	4	1.03	-0.90	-34.0	-39.2	-6.50	-6.00	0.73	1	17.30	4.80	0.05	0.74	-0.34	-20.2	LUNG CANCER	CHEMO	SYMPTOMATIC BUT AMBULATORY	CANCER RRECURRENCE	SEVERE	DOSE REDUCED	3	0	-0.502580577	0.369233200	17
DEATH	3	FEMALE	68	III	-1.29	-0.52	-1.60	79	-0.02	0.05	-23.0	9.5	-5.80	-9.20	0.14	17	25.78	3.10	1.13	1.83	-0.87	18.5	SARCOMA	CHEMO	ASYMTOMATIC	ATEROESCLEROSIS	MODERATE	DRUG INTERRUPTED	9	7	0.688749725	1.020032227	16
DEATH	6	FEMALE	67	III	-2.68	-1.02	0.60	-258	-0.22	-0.17	-5.0	-2.6	-0.01	0.62	0.25	-6	19.07	8.70	0.12	0.39	0.77	2.7	HEAD AND NECK	CHEMO	SYMPTOMATIC BUT AMBULATORY	DIABETES MELLITUS	NO SIDE EFFECTS	DOSE REDUCED	1	0	0.217516526	0.289366507	47
DEATH	10	MALE	87	III	2.06	-0.29	-1.00	197	-2.64	0.20	-65.0	9.3	13.20	3.30	-0.28	-1	23.76	6.60	0.05	-0.45	-0.38	65.2	GASTRIC CANCER	CHEMO	SYMPTOMATIC BUT AMBULATORY	HYPERTENSION	SEVERE	DOSE REDUCED	10	33	-0.256666667	-0.538333333	30
DEATH	4	MALE	78	III	-0.31	-0.29	-0.90	31	-0.15	-0.16	5.0	-26.4	-3.50	-10.70	0.48	-2	19.86	3.60	0.33	0.41	-0.35	38.9	LUNG CANCER	CHEMO	BEDBOUND	CHRONIC INFLAMMATION	MODERATE	DRUG INTERRUPTED	8	7	-0.135169763	0.376817502	32
DEATH	8	FEMALE	53	I	-0.36	-0.45	-1.20	-40	0.39	-0.11	5.0	-5.9	1.70	-3.00	0.05	18	19.26	4.60	0.08	-0.13	-0.16	24.0	PANCREAS CANCER	CHEMO	SYMPTOMATIC BUT AMBULATORY	HYPERLIPIDEMIA	SLIGTHLY SIDE EFFCTS	DOSE REDUCED	3	7	0.162247681	-0.019717871	23

datos<-as.data.frame(datos)
datos

A data.frame: 59 × 34

STATUS	CYCLES_BETWEEN_PET1_PET2	GENDER	AGE	TNM_STAGE	DIFF_WBC	DIFF_RBC	DIFF_HB	DIFF_PLT	DIFF_CRP	DIFF_ALBUMIN	DIFF_LDH	DIFF_eGFR	DIFF_AST	DIFF_ALT	DIFF_K	DIFF_BGL	BMI	DIFF_BW	DIFF_SPLEEN_UPTAKE	DIFF_BM_UPTAKE	DIFF_LIVER_UPTAKE	DIFF_ESTIMATED_SPLEEN_VOL	DIAGNOSTIC	TREATMENT	ECOGPS	COMORBIDITIES	CTCNCI	ACTION_TAKEN_	TIME_BETWEE_PET	dias	DIFF_SLR	DIFF_BMLR	OVERALL_TIME
<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1	3	2	54	3	-1.83	-0.64	-2.20	-82	-0.56	0.98	-271.0	23.7	0.30	13.20	0.10	12	22.90	-4.00	0.02	0.12	0.44	-80.9	3	2	4	5	4	5	2	25	0.029335236	0.109059370	2
1	2	1	79	4	-3.08	0.14	0.70	-159	-3.80	1.33	22.0	-0.2	4.40	5.30	0.58	-14	16.47	0.30	-0.20	-0.60	-0.30	6.2	4	2	1	6	3	5	4	0	-0.156566790	-0.046889612	50
1	3	2	60	3	-3.18	-0.50	3.54	22	-0.06	-0.20	64.2	4.6	-2.60	1.10	0.33	12	20.15	-8.10	-0.12	0.34	0.27	9.1	5	2	5	4	2	4	4	14	-0.044698028	-0.365688558	2
1	3	2	76	2	-1.28	0.79	1.70	9	0.04	-0.12	-55.0	-8.9	-0.70	-6.70	0.05	-4	22.83	0.00	0.46	-0.40	0.59	-38.8	8	2	1	6	3	3	6	0	-0.002886671	0.130612972	12
1	5	1	70	2	0.00	-0.09	-0.40	-95	-7.23	0.05	6.0	-13.2	2.70	2.20	0.06	-8	18.29	-0.10	-0.34	-0.80	-0.48	-87.6	6	2	4	4	3	3	6	0	-0.095215760	-0.252680247	12
1	4	2	54	4	0.99	0.10	0.00	-3	0.02	-0.16	-4.0	-13.1	0.60	2.80	-0.47	-3	27.18	-10.80	0.00	-0.10	-0.53	34.4	5	2	1	2	1	3	6	363	-0.167591661	0.072513788	18
1	2	2	60	3	-1.48	-1.23	-1.90	-60	0.02	-0.46	-16.0	14.1	5.20	0.60	-0.26	-12	22.76	1.40	-0.07	0.11	-0.17	-85.0	5	2	1	3	3	3	9	0	0.114290017	0.200158109	7
1	5	2	69	3	-1.65	0.04	0.60	-354	4.66	0.52	-333.0	-9.2	-7.30	-23.90	-0.02	-70	17.36	-2.20	0.61	-0.18	0.61	11.3	4	2	5	4	2	3	9	95	0.223201621	0.314140976	16
1	6	2	64	4	3.89	-0.78	-3.20	242	15.21	-1.22	-28.0	9.0	-10.40	-13.20	-0.23	70	23.65	-11.10	0.30	0.50	0.03	8.1	4	2	4	5	5	3	10	28	0.163665694	0.070517745	65
1	12	2	72	3	-0.80	-0.37	-0.90	-26	-0.02	-0.09	-13.0	-2.1	0.00	-1.90	0.26	64	22.84	-6.60	0.24	0.48	0.65	-51.4	4	2	1	3	3	3	16	0	-0.117647059	0.083556150	1
1	29	2	71	3	-0.91	0.07	0.20	11	0.18	0.18	52.0	-8.7	-0.60	-0.10	0.59	11	21.34	0.00	-0.05	0.69	0.09	-46.1	4	2	1	6	3	1	21	0	0.312678365	0.619776214	41
1	6	2	67	3	-2.67	-0.86	2.30	-4	-0.36	-0.72	69.0	16.9	5.00	3.80	0.05	11	20.29	-1.10	0.20	0.70	0.52	66.9	5	2	1	2	3	3	25	0	-0.160987704	-0.061448788	11
1	48	2	60	3	4.52	0.11	-1.90	-63	17.83	-0.61	-15.0	-24.1	-4.10	-4.40	-0.67	64	29.11	-6.20	1.13	2.08	0.31	177.0	8	2	1	5	5	7	25	735	0.413640470	-0.037127371	11
1	20	2	61	4	1.71	0.89	3.70	-183	-1.24	1.74	86.0	-9.4	-0.50	1.80	-0.17	-11	20.12	0.00	0.15	-0.22	0.57	-44.5	6	2	5	4	3	7	38	0	0.125985126	0.396964147	15
2	2	2	83	2	2.37	-0.12	1.60	44	0.00	0.00	0.0	0.0	0.00	0.00	0.00	14	24.46	-1.50	0.10	0.35	-0.03	31.6	6	2	1	2	3	3	11	0	0.253714576	0.288722186	4
2	3	2	68	1	3.22	-1.11	0.90	-59	3.02	-0.09	5.0	-19.6	-11.60	-7.50	0.17	6	24.41	-4.30	-0.05	-0.45	0.27	-2.1	6	2	5	2	3	3	2	0	0.207638889	0.081944444	13
2	18	2	77	2	0.37	0.63	1.90	-24	0.22	-0.06	46.0	7.5	6.50	4.10	0.48	-12	18.07	0.00	-0.13	0.20	-0.14	-2.4	8	2	1	3	4	2	10	3	-0.117647059	0.083556150	12
2	4	2	60	3	-1.23	-0.28	-1.20	-11	2.69	0.10	194.0	-10.2	10.30	7.50	-0.01	1	23.14	4.00	-0.42	-0.40	-0.74	55.2	5	2	4	3	2	7	15	441	0.030665281	0.133264033	29
2	7	2	64	4	0.46	-0.41	-1.50	34	0.31	-0.44	-8.0	-4.6	-1.10	0.40	0.44	-6	22.12	5.70	0.27	0.46	-0.37	15.1	4	2	5	4	2	5	4	52	0.135997522	0.200123916	42
2	15	2	67	2	-4.64	0.71	2.40	-189	-15.88	1.57	-510.0	-24.5	-2.90	-16.90	0.00	-27	14.08	44.02	-0.40	-0.19	0.19	2.4	4	2	4	4	5	5	13	364	-0.513598150	-0.140500210	21
2	5	2	83	4	1.13	-0.21	-0.60	16	0.32	0.08	-9.0	-2.8	0.70	0.40	0.59	16	20.07	5.90	0.22	-0.21	0.34	-85.5	4	2	5	6	3	3	8	0	-0.193627043	-0.093738849	21
2	4	1	56	2	7.74	-0.33	-0.80	463	-2.03	2.83	-140.0	45.3	-219.60	-250.80	-0.90	-18	21.79	-2.30	-0.16	0.26	-0.84	34.0	6	2	4	5	1	4	9	233	-0.045342594	0.034875129	8
1	7	2	65	2	-1.68	-0.68	1.00	0	7.55	-0.13	8.0	-3.3	-6.90	-5.70	0.04	14	18.18	12.20	0.31	-0.54	-0.85	22.3	7	1	1	4	3	7	1	0	0.004147110	0.162187359	27
1	5	1	55	4	-3.77	-1.93	1.40	-58	0.12	0.01	-29.0	6.7	-3.10	-10.20	0.48	25	25.60	4.60	0.40	0.59	-0.24	29.9	3	1	5	5	4	5	2	20	-0.230769231	-0.330769231	10
1	2	1	75	3	-2.17	-0.94	-2.30	-100	0.00	0.27	21.0	-10.4	1.80	-2.40	-0.20	8	25.96	-5.40	0.19	0.65	0.88	-1.0	5	1	4	6	2	4	2	15	0.071091909	0.511586803	41
1	6	2	74	3	-3.24	-1.16	-0.20	100	-0.46	-0.56	41.0	3.0	4.40	0.80	0.68	9	20.85	9.30	0.00	-0.20	0.60	19.0	7	1	1	6	2	4	3	35	0.509097867	0.149792993	3
1	3	1	76	4	-0.04	-0.43	-0.80	95	0.02	-0.10	103.0	-2.2	7.90	0.10	-0.33	2	20.90	-2.40	-0.12	0.84	-0.27	19.7	5	1	1	4	3	3	3	0	0.123854660	0.028606149	32
1	3	2	77	3	-5.70	-1.29	-2.60	-229	0.18	-0.21	45.0	-9.7	-19.70	-84.70	-0.10	-13	22.94	0.00	0.03	-0.33	0.84	98.8	5	1	1	6	1	4	3	6	0.047663153	-0.219056440	15
1	4	2	68	2	-0.47	0.05	5.80	69	-2.74	1.18	67.0	-16.2	1.20	-0.70	0.76	-20	21.57	5.40	0.26	0.55	0.46	4.1	5	1	1	6	3	3	4	0	-0.026468254	0.043273810	14
1	2	2	68	2	-2.47	-0.73	-1.00	-61	-0.10	-0.12	25.0	13.4	-0.70	-2.10	0.19	-13	35.81	-1.30	-0.05	0.43	-0.07	15.1	5	1	4	4	3	3	4	0	-0.318764676	-0.090749584	15
1	3	1	86	4	-4.78	-1.30	-3.50	-17	-6.38	-0.12	-2.0	-3.0	-15.20	-19.30	0.23	-8	26.75	-2.40	0.30	0.19	0.35	41.0	10	1	5	6	4	4	5	12	0.243005526	0.290715505	24
1	2	2	74	2	1.13	-0.30	-1.70	-32	0.27	-0.25	-12.0	1.2	-5.60	-5.60	-0.07	21	19.62	15.60	0.20	-0.30	-0.10	56.3	7	1	5	5	4	4	5	0	-0.005084746	-0.015906128	6
1	3	1	52	1	3.14	0.03	0.40	-9	0.07	0.05	91.0	8.9	4.30	16.80	0.09	9	22.19	-4.80	-0.37	0.77	-0.08	42.4	1	1	1	4	3	3	5	0	0.087355212	-0.170045045	32
1	4	1	74	4	2.19	0.44	1.30	59	-0.01	-0.24	40.0	1.9	-3.20	-8.60	0.14	24	17.78	5.60	-0.16	-0.01	-0.23	-1.8	4	1	4	6	2	3	6	1	0.105820106	-0.078042328	23
1	13	1	70	4	-1.92	-0.90	-1.60	-233	-1.94	1.15	-294.0	-3.3	-18.70	-6.40	-0.19	20	24.33	-1.20	0.08	-1.01	0.22	-55.6	3	1	5	5	3	7	7	0	0.083963069	0.159957950	15
1	2	2	62	3	1.53	-0.61	-0.40	-19	-0.05	-0.21	-5.0	-12.8	0.10	-4.20	0.88	19	16.24	0.80	-0.08	0.08	0.01	5.8	5	1	1	3	3	3	8	0	-0.244661571	-0.313686865	63
1	3	2	79	4	4.23	0.38	1.20	170	0.59	-0.50	-66.0	3.3	-111.50	-18.30	0.24	-118	20.64	8.90	-0.66	-0.76	-1.59	-9.1	8	1	1	3	3	3	9	0	0.202321083	0.496711799	9
1	14	1	69	2	3.39	-1.31	-2.20	-59	-0.09	-0.60	16.0	34.3	-4.70	-13.80	0.10	-41	18.57	0.80	-0.39	0.06	-0.70	99.4	7	1	3	1	3	4	9	0	0.061267406	-0.157317420	4
1	10	1	65	2	-2.68	0.18	1.80	316	4.78	-1.70	14.0	47.7	4.70	-1.20	-0.56	15	16.51	15.10	0.22	-0.36	0.02	46.9	3	1	5	5	4	4	10	32	-0.054315003	-0.256317546	21
1	4	2	74	3	1.51	0.83	2.60	-110	-0.01	0.22	-81.0	-30.1	-11.50	-9.90	0.14	-7	22.08	-1.00	0.09	-0.12	0.18	-18.1	5	1	1	6	2	3	12	36	-0.159000071	-0.136205070	3
1	12	2	76	3	-0.49	-0.11	-0.40	-56	0.04	-0.01	31.0	-13.4	7.20	5.20	0.36	-56	26.80	-2.20	0.24	0.44	0.63	-110.2	5	1	4	1	4	1	14	51	-0.040974878	0.214845830	6
1	4	1	72	3	2.00	0.84	2.40	-16	-0.01	0.22	1.0	1.4	-6.70	-4.20	0.06	2	15.43	4.50	-0.80	0.90	0.14	18.0	1	1	1	2	3	1	17	0	-0.292250233	-0.619234360	20
1	14	1	76	3	3.86	1.15	3.80	15	-3.88	-1.50	30.0	-7.6	18.90	18.30	-0.10	-23	29.56	3.60	0.66	0.00	0.50	26.7	3	1	5	6	3	7	19	0	-0.314585877	-0.182208995	20
1	4	1	56	3	-3.11	-0.22	-0.50	4	-0.10	-0.08	9.0	-20.0	-2.90	-1.60	-0.03	43	18.03	-1.30	-0.63	0.21	0.49	2.6	5	1	4	4	3	7	20	0	0.027777778	-0.125000000	1
1	4	2	70	3	-0.19	-0.44	-0.70	-17	-0.27	0.13	4.0	-5.7	-7.60	-12.00	0.36	-160	23.75	-5.60	-0.55	-1.20	0.10	31.0	5	1	5	3	3	3	20	0	-0.095525727	-0.514605305	3
1	4	1	75	3	-2.30	0.14	0.40	-82	0.02	-0.31	16.0	-25.1	-6.10	-9.40	0.73	0	25.78	-4.90	0.10	0.20	-0.29	2.4	3	1	1	6	4	4	33	312	0.048263534	0.141343207	5
2	9	2	81	2	0.10	-0.19	-1.10	1	1.19	-0.06	-4.0	-2.2	-5.50	-8.80	1.07	16	16.17	0.00	0.34	-0.82	-0.65	-68.5	2	1	1	2	2	5	9	86	0.226870727	0.438938719	22
2	9	2	73	3	-1.94	0.13	0.40	75	-1.67	-2.28	32.0	-32.7	15.60	-2.40	0.51	-1	16.90	26.20	0.20	0.70	0.52	-3.1	7	1	4	5	4	5	4	195	0.065394796	0.097268444	16
2	5	2	77	1	-2.19	-1.97	-5.20	-151	0.39	0.12	34.0	3.8	11.90	19.40	-0.64	30	25.21	5.30	-0.14	-0.72	0.30	-7.6	5	1	1	3	3	6	5	0	-0.080303030	-0.620707071	9
2	6	2	74	1	5.48	-0.77	-2.80	15	7.21	-0.37	472.0	6.8	-1.10	-2.60	0.35	29	23.51	7.50	0.34	0.01	-0.32	-5.6	2	1	1	2	3	3	12	0	-0.022968112	-0.097195217	1
2	6	2	70	1	0.38	0.59	0.20	-9	0.25	-0.05	18.0	-6.3	-0.80	-8.60	0.63	17	16.68	18.70	-0.07	0.09	-0.01	33.1	5	1	4	5	4	4	12	50	-0.136822783	-0.321107069	13
2	6	2	63	2	2.42	0.49	4.00	26	2.96	-1.35	170.0	9.9	-55.90	-84.90	-0.34	-70	17.89	24.10	-0.06	0.21	-0.25	84.2	7	1	5	4	3	4	7	0	-0.195751634	-0.070728291	64
2	5	1	61	2	-0.96	-1.44	-2.70	-12	0.03	-0.12	15.0	-0.9	6.60	14.30	0.91	29	20.44	6.90	-0.41	0.24	0.51	34.2	3	1	1	5	1	4	11	114	-0.101647059	0.002627451	12
2	2	2	79	3	-2.67	-0.86	-3.50	4	1.03	-0.90	-34.0	-39.2	-6.50	-6.00	0.73	1	17.30	4.80	0.05	0.74	-0.34	-20.2	5	1	4	2	4	4	3	0	-0.502580577	0.369233200	17
2	3	1	68	3	-1.29	-0.52	-1.60	79	-0.02	0.05	-23.0	9.5	-5.80	-9.20	0.14	17	25.78	3.10	1.13	1.83	-0.87	18.5	9	1	1	1	2	5	9	7	0.688749725	1.020032227	16
2	6	1	67	3	-2.68	-1.02	0.60	-258	-0.22	-0.17	-5.0	-2.6	-0.01	0.62	0.25	-6	19.07	8.70	0.12	0.39	0.77	2.7	4	1	4	4	3	4	1	0	0.217516526	0.289366507	47
2	10	2	87	3	2.06	-0.29	-1.00	197	-2.64	0.20	-65.0	9.3	13.20	3.30	-0.28	-1	23.76	6.60	0.05	-0.45	-0.38	65.2	2	1	4	6	4	4	10	33	-0.256666667	-0.538333333	30
2	4	2	78	3	-0.31	-0.29	-0.90	31	-0.15	-0.16	5.0	-26.4	-3.50	-10.70	0.48	-2	19.86	3.60	0.33	0.41	-0.35	38.9	5	1	2	3	2	5	8	7	-0.135169763	0.376817502	32
2	8	1	53	1	-0.36	-0.45	-1.20	-40	0.39	-0.11	5.0	-5.9	1.70	-3.00	0.05	18	19.26	4.60	0.08	-0.13	-0.16	24.0	7	1	4	5	5	4	3	7	0.162247681	-0.019717871	23

Exploracion inicial

# Tipos de varibales                    
aux = as.data.frame(t(t(sapply(datos, class))))
colnames(aux) <- "Tipo de Variable"
aux$Numero_Columna = seq(nrow(aux))
aux

A data.frame: 34 × 2

	Tipo de Variable	Numero_Columna
	<chr>	<int>
STATUS	character	1
CYCLES_BETWEEN_PET1_PET2	numeric	2
GENDER	character	3
AGE	numeric	4
TNM_STAGE	character	5
DIFF_WBC	numeric	6
DIFF_RBC	numeric	7
DIFF_HB	numeric	8
DIFF_PLT	numeric	9
DIFF_CRP	numeric	10
DIFF_ALBUMIN	numeric	11
DIFF_LDH	numeric	12
DIFF_eGFR	numeric	13
DIFF_AST	numeric	14
DIFF_ALT	numeric	15
DIFF_K	numeric	16
DIFF_BGL	numeric	17
BMI	numeric	18
DIFF_BW	numeric	19
DIFF_SPLEEN_UPTAKE	numeric	20
DIFF_BM_UPTAKE	numeric	21
DIFF_LIVER_UPTAKE	numeric	22
DIFF_ESTIMATED_SPLEEN_VOL	numeric	23
DIAGNOSTIC	character	24
TREATMENT	character	25
ECOGPS	character	26
COMORBIDITIES	character	27
CTCNCI	character	28
ACTION_TAKEN_	character	29
TIME_BETWEE_PET	numeric	30
dias	numeric	31
DIFF_SLR	numeric	32
DIFF_BMLR	numeric	33
OVERALL_TIME	numeric	34

overview(datos)  

plot(overview(datos))

A overview: 16 × 3

division	metrics	value
<chr>	<chr>	<dbl>
size	observations	59
size	variables	34
size	values	2006
size	memory size	23968
duplicated	duplicate observation	0
missing	complete observation	59
missing	missing observation	0
missing	missing variables	0
missing	missing values	0
data type	numerics	25
data type	integers	0
data type	factors/ordered	0
data type	characters	9
data type	Dates	0
data type	POSIXcts	0
data type	others	0

ORDENAR Y LIMPIAR DATOS

No quitamos ni outliers ni extremos ya que son pocas observaciones y ademas son mediciones reales

Transformar tipos de variables

# pasar variables a factor
datos$STATUS <- as.factor(datos$STATUS)
datos$GENDER <- as.factor(datos$GENDER)
datos$COMORBIDITIES <- as.factor(datos$COMORBIDITIES)
datos$CTCNCI <- as.factor(datos$CTCNCI)
datos$ACTION_TAKEN_ <- as.factor(datos$ACTION_TAKEN_)
datos$TNM_STAGE <- as.factor(datos$TNM_STAGE)
datos$DIAGNOSTIC <- as.factor(datos$DIAGNOSTIC)
datos$TREATMENT <- as.factor(datos$TREATMENT)
datos$ECOGPS <- as.factor(datos$ECOGPS)

# vemos los cambios
aux = as.data.frame(t(t(sapply(datos, class))))
colnames(aux) <- "Tipo de Variable"
aux$Numero_Columna = seq(nrow(aux))
aux

A data.frame: 34 × 2

	Tipo de Variable	Numero_Columna
	<chr>	<int>
STATUS	factor	1
CYCLES_BETWEEN_PET1_PET2	numeric	2
GENDER	factor	3
AGE	numeric	4
TNM_STAGE	factor	5
DIFF_WBC	numeric	6
DIFF_RBC	numeric	7
DIFF_HB	numeric	8
DIFF_PLT	numeric	9
DIFF_CRP	numeric	10
DIFF_ALBUMIN	numeric	11
DIFF_LDH	numeric	12
DIFF_eGFR	numeric	13
DIFF_AST	numeric	14
DIFF_ALT	numeric	15
DIFF_K	numeric	16
DIFF_BGL	numeric	17
BMI	numeric	18
DIFF_BW	numeric	19
DIFF_SPLEEN_UPTAKE	numeric	20
DIFF_BM_UPTAKE	numeric	21
DIFF_LIVER_UPTAKE	numeric	22
DIFF_ESTIMATED_SPLEEN_VOL	numeric	23
DIAGNOSTIC	factor	24
TREATMENT	factor	25
ECOGPS	factor	26
COMORBIDITIES	factor	27
CTCNCI	factor	28
ACTION_TAKEN_	factor	29
TIME_BETWEE_PET	numeric	30
dias	numeric	31
DIFF_SLR	numeric	32
DIFF_BMLR	numeric	33
OVERALL_TIME	numeric	34

Identificar la variable respuesta

# Identificar variable respuesta
respuesta <- c("STATUS")
respuesta

'STATUS'

Identificar las variables cuantitativas de entrada

# Identificar variables numericas de entrada
tipos_var <- t(t(sapply(datos, class)))
var_num <- colnames(datos)[tipos_var=="integer"|tipos_var=="numeric"]
var_num <- var_num[var_num!=respuesta]
as.data.frame(var_num)

A data.frame: 25 × 1

var_num
<chr>
CYCLES_BETWEEN_PET1_PET2
AGE
DIFF_WBC
DIFF_RBC
DIFF_HB
DIFF_PLT
DIFF_CRP
DIFF_ALBUMIN
DIFF_LDH
DIFF_eGFR
DIFF_AST
DIFF_ALT
DIFF_K
DIFF_BGL
BMI
DIFF_BW
DIFF_SPLEEN_UPTAKE
DIFF_BM_UPTAKE
DIFF_LIVER_UPTAKE
DIFF_ESTIMATED_SPLEEN_VOL
TIME_BETWEE_PET
dias
DIFF_SLR
DIFF_BMLR
OVERALL_TIME

Identificar las variables cualitativas de entrada

# Identificar variables cualitativas
tipos_var <- t(t(sapply(datos, class)))
var_cual <- colnames(datos)[tipos_var=="character"|tipos_var=="factor"]
var_cual <- var_cual[var_cual!=respuesta]
as.data.frame(var_cual)

A data.frame: 8 × 1

var_cual
<chr>
GENDER
TNM_STAGE
DIAGNOSTIC
TREATMENT
ECOGPS
COMORBIDITIES
CTCNCI
ACTION_TAKEN_

EXPLORACION UNIVARIADA DE LOS DATOS

Describir la variable respuesta

# Tabla de frecuencias:

as.data.frame(table(datos$STATUS))

A data.frame: 2 × 2

Var1	Freq
<fct>	<int>
ALIVE	38
DEATH	21

# Barplot
barplot(table(datos$STATUS))

Descripcion variables cuantitativas

Descripcion estadistica variables cuantitativas de entrada

# Descripcion de variables cuantitativas:
d_uni<-dlookr::describe(datos)
d_uni

A tibble: 25 × 26

described_variables	n	na	mean	sd	se_mean	IQR	skewness	kurtosis	p00	p01	p05	p10	p20	p25	p30	p40	p50	p60	p70	p75	p80	p90	p95	p99	p100
<chr>	<int>	<int>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
CYCLES_BETWEEN_PET1_PET2	59	0	7.06779661	7.4506755	0.96999533	4.5000000	3.5461362	16.079740747	2.0000000	2.0000000	2.0000000	2.0000000	3.0000000	3.0000000	3.40000000	4.00000000	5.000000000	5.80000000	6.00000000	7.5000000	9.4000000	14.0000000	18.2000000	36.9800000	48.0000000
AGE	59	0	69.38983051	8.6223786	1.12253807	12.5000000	-0.1966440	-0.589411859	52.0000000	52.5800000	54.0000000	56.0000000	61.0000000	63.5000000	65.00000000	68.00000000	70.000000000	72.80000000	74.60000000	76.0000000	76.4000000	79.0000000	83.0000000	86.4200000	87.0000000
DIFF_WBC	59	0	-0.17525424	2.7263955	0.35494646	3.8000000	0.5057711	0.129045347	-5.7000000	-5.1664000	-3.8570000	-3.1240000	-2.5500000	-2.1800000	-1.88400000	-1.27000000	-0.470000000	0.08000000	1.13000000	1.6200000	2.1120000	3.4840000	4.2590000	6.4292000	7.7400000
DIFF_RBC	59	0	-0.31101695	0.7000066	0.09113310	0.8950000	-0.1435844	-0.229988897	-1.9700000	-1.9468000	-1.3230000	-1.2420000	-0.8760000	-0.7750000	-0.66400000	-0.43800000	-0.290000000	-0.13400000	0.06200000	0.1200000	0.1560000	0.6460000	0.8310000	0.9992000	1.1500000
DIFF_HB	59	0	-0.09593220	2.1214367	0.27618753	2.8000000	0.3373839	0.254904377	-5.2000000	-4.2140000	-3.2300000	-2.6200000	-1.7800000	-1.5500000	-1.16000000	-0.80000000	-0.400000000	0.36000000	0.82000000	1.2500000	1.6400000	2.4400000	3.7100000	4.7560000	5.8000000
DIFF_PLT	59	0	-13.27118644	129.0645161	16.80276880	84.5000000	0.7237460	3.418977080	-354.0000000	-298.3200000	-229.4000000	-163.8000000	-82.0000000	-60.5000000	-58.60000000	-23.00000000	-11.000000000	0.80000000	15.00000000	24.0000000	38.0000000	96.0000000	201.5000000	377.7400000	463.0000000
DIFF_CRP	59	0	0.33372881	4.4082997	0.57391174	0.5600000	0.9022780	8.168553321	-15.8800000	-10.8630000	-4.1300000	-2.6600000	-0.5000000	-0.2450000	-0.10000000	-0.01800000	0.020000000	0.04000000	0.23800000	0.3150000	0.4700000	3.3480000	7.2440000	16.3104000	17.8300000
DIFF_ALBUMIN	59	0	-0.05762712	0.7953146	0.10354114	0.3900000	0.6459202	3.439907176	-2.2800000	-1.9436000	-1.3650000	-0.7560000	-0.4480000	-0.2800000	-0.21000000	-0.12800000	-0.100000000	-0.05200000	0.05000000	0.1100000	0.1880000	1.0140000	1.3540000	2.1978000	2.8300000
DIFF_LDH	59	0	-2.79322034	125.8579055	16.38530365	47.0000000	-0.8048823	7.846212457	-510.0000000	-407.3400000	-273.3000000	-69.0000000	-25.0000000	-14.0000000	-8.60000000	-1.60000000	5.000000000	15.80000000	28.00000000	33.0000000	42.6000000	72.4000000	109.7000000	310.7600000	472.0000000
DIFF_eGFR	59	0	-2.49830508	16.4224247	2.13801758	15.9500000	0.6730682	1.790030255	-39.2000000	-35.4300000	-26.7700000	-24.1800000	-13.1400000	-10.3000000	-9.32000000	-5.86000000	-2.800000000	-0.34000000	3.18000000	5.6500000	8.0600000	13.5400000	24.7600000	46.3080000	47.7000000
DIFF_AST	59	0	-7.27813559	33.1784402	4.31946500	9.4500000	-5.2484541	30.880490044	-219.6000000	-156.9020000	-23.3200000	-12.3200000	-6.7800000	-5.9500000	-5.18000000	-2.90000000	-0.700000000	0.00000000	1.50000000	3.5000000	4.5200000	7.3400000	12.0300000	16.9860000	18.9000000
DIFF_ALT	59	0	-9.48949153	36.3504299	4.73242289	9.9500000	-5.4936828	34.708950221	-250.8000000	-154.5780000	-29.9800000	-17.1800000	-10.0200000	-9.0000000	-8.16000000	-5.36000000	-2.400000000	-0.80000000	0.52000000	0.9500000	2.4400000	5.7400000	14.5500000	18.7620000	19.4000000
DIFF_K	59	0	0.14796610	0.4109064	0.05349546	0.5450000	-0.1046069	-0.003291498	-0.9000000	-0.7666000	-0.5680000	-0.3320000	-0.1780000	-0.0850000	-0.01600000	0.05000000	0.100000000	0.22200000	0.35600000	0.4600000	0.4920000	0.6900000	0.7720000	0.9772000	1.0700000
DIFF_BGL	59	0	-1.27118644	37.1413101	4.83538671	28.0000000	-1.8484106	6.368310476	-160.0000000	-135.6400000	-70.0000000	-29.8000000	-13.0000000	-11.5000000	-7.60000000	-1.80000000	2.000000000	10.60000000	14.60000000	16.5000000	18.4000000	29.0000000	45.1000000	66.5200000	70.0000000
BMI	59	0	21.54457627	4.0359947	0.52544175	5.5200000	0.7826975	1.399355371	14.0800000	14.8630000	16.2330000	16.6460000	17.9740000	18.2350000	19.14600000	20.17800000	21.340000000	22.64600000	23.36200000	23.7550000	24.4300000	26.1180000	27.3730000	32.1850000	35.8100000
DIFF_BW	59	0	3.00033898	9.1860843	1.19592631	7.8500000	2.0129510	6.438370916	-11.1000000	-10.9260000	-6.7500000	-5.4400000	-2.4000000	-2.2000000	-1.30000000	0.00000000	0.300000000	3.60000000	5.10000000	5.6500000	6.7200000	12.7800000	19.2400000	33.6844000	44.0200000
DIFF_SPLEEN_UPTAKE	59	0	0.05067797	0.3633743	0.04730731	0.3650000	0.4149319	1.664027013	-0.8000000	-0.7188000	-0.5580000	-0.4020000	-0.1600000	-0.1250000	-0.07600000	-0.04000000	0.050000000	0.11600000	0.21200000	0.2400000	0.2820000	0.3520000	0.6150000	1.1300000	1.1300000
DIFF_BM_UPTAKE	59	0	0.10711864	0.6017591	0.07834236	0.7100000	0.6111772	1.721229694	-1.2000000	-1.0898000	-0.8020000	-0.6240000	-0.3760000	-0.2600000	-0.19600000	-0.00800000	0.110000000	0.21000000	0.40200000	0.4500000	0.5200000	0.7080000	0.8460000	1.9350000	2.0800000
DIFF_LIVER_UPTAKE	59	0	0.01779661	0.5004070	0.06514744	0.7450000	-0.5918683	0.488986741	-1.5900000	-1.1724000	-0.8410000	-0.6600000	-0.3440000	-0.2950000	-0.24600000	-0.09600000	0.020000000	0.18800000	0.32800000	0.4500000	0.5040000	0.6020000	0.6620000	0.8568000	0.8800000
DIFF_ESTIMATED_SPLEEN_VOL	59	0	8.61694915	50.0321902	6.51363635	40.1500000	0.1688579	1.731668976	-110.2000000	-97.0920000	-85.0500000	-58.1800000	-18.9400000	-6.6000000	-2.28000000	2.62000000	9.100000000	18.90000000	30.56000000	33.5500000	36.2000000	58.0800000	85.6600000	131.9920000	177.0000000
TIME_BETWEE_PET	59	0	9.62711864	7.6404060	0.99469613	8.0000000	1.6870456	3.317725303	1.0000000	1.0000000	2.0000000	2.8000000	4.0000000	4.0000000	5.00000000	6.00000000	8.000000000	9.00000000	10.60000000	12.0000000	13.4000000	20.0000000	25.0000000	35.1000000	38.0000000
dias	59	0	57.15254237	133.9282893	17.43597813	34.0000000	3.2949717	12.075154478	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.00000000	0.00000000	0.000000000	7.00000000	26.80000000	34.0000000	50.4000000	202.6000000	363.1000000	564.4800000	735.0000000
DIFF_SLR	59	0	0.00266275	0.2193508	0.02855705	0.2609162	0.3392356	1.204043701	-0.5135982	-0.5072080	-0.3150038	-0.2470626	-0.1597951	-0.1359963	-0.11124706	-0.04521368	-0.002886671	0.04814346	0.09843415	0.1249199	0.1628149	0.2300977	0.3227746	0.5845516	0.6887497
DIFF_BMLR	59	0	0.02945905	0.3024081	0.03937018	0.3195083	0.3246088	1.244142990	-0.6207071	-0.6198529	-0.5169781	-0.3230395	-0.1749106	-0.1383526	-0.09581267	-0.04493716	0.034875129	0.08355615	0.14641308	0.1811556	0.2443964	0.3808468	0.4981993	0.7878837	1.0200322
OVERALL_TIME	59	0	19.10169492	15.8415592	2.06239534	15.0000000	1.3537447	1.563910333	1.0000000	1.0000000	1.9000000	3.0000000	6.0000000	8.5000000	10.40000000	12.20000000	15.000000000	17.80000000	21.60000000	23.5000000	29.4000000	41.2000000	51.3000000	64.4200000	65.0000000

Descripcion grafica variables numericas

for (i in 1:length(var_num)) {
  name_i <- as.name(var_num[i])
  r <- list()
  
  # Histograma de densidad
  r[[1]]<-ggplot(datos, aes(x=!!name_i)) +
    geom_density(fill="#69b3a2", color="black", alpha=0.6)+
    theme_light() +
    #theme(legend.position = "none") +
    xlab("") +
    ylab("Densidad de Frecuencia")
  
  # Boxplot
  r[[2]]<-ggplot(data=datos, aes(y=!!name_i)) +
    geom_boxplot(size = 0.4) +
    theme_light() +
    theme(legend.position = "none")+
    xlab("")
  
  # Qqplot
  r[[3]]<-ggqqplot(datos, x = var_num[i],color = "#FF6666",add.params = list(color = "black"))+
    xlab("") + ylab("Cuartiles reales") +
    theme_minimal() +
    #ggtitle("qqplot") +
    theme(plot.title = element_text(hjust = 0.5))
  
  grid.arrange(r[[1]], r[[2]], r[[3]],
               nrow=1, ncol = 3,
               top = textGrob(var_num[i],gp=gpar(fontsize=16,font=3)))
}

Analisis de outliers(podemos ver algun dato que nos resulte interesante)

# Analisis de outliers:
diag_out <- diagnose_outlier(datos)
diag_out

A tibble: 25 × 6

variables	outliers_cnt	outliers_ratio	outliers_mean	with_mean	without_mean
<chr>	<int>	<dbl>	<dbl>	<dbl>	<dbl>
CYCLES_BETWEEN_PET1_PET2	5	8.474576	26.00000000	7.06779661	5.314814815
AGE	0	0.000000	NaN	69.38983051	69.389830508
DIFF_WBC	1	1.694915	7.74000000	-0.17525424	-0.311724138
DIFF_RBC	0	0.000000	NaN	-0.31101695	-0.311016949
DIFF_HB	1	1.694915	5.80000000	-0.09593220	-0.197586207
DIFF_PLT	10	16.949153	12.50000000	-13.27118644	-18.530612245
DIFF_CRP	21	35.593220	0.84142857	0.33372881	0.053157895
DIFF_ALBUMIN	13	22.033898	0.14076923	-0.05762712	-0.113695652
DIFF_LDH	8	13.559322	-89.00000000	-2.79322034	10.729411765
DIFF_eGFR	4	6.779661	22.02500000	-2.49830508	-4.281818182
DIFF_AST	4	6.779661	-92.02500000	-7.27813559	-1.114727273
DIFF_ALT	6	10.169492	-60.98333333	-9.48949153	-3.660000000
DIFF_K	0	0.000000	NaN	0.14796610	0.147966102
DIFF_BGL	8	13.559322	-34.50000000	-1.27118644	3.941176471
BMI	1	1.694915	35.81000000	21.54457627	21.298620690
DIFF_BW	4	6.779661	28.25500000	3.00033898	1.163636364
DIFF_SPLEEN_UPTAKE	3	5.084746	0.48666667	0.05067797	0.027321429
DIFF_BM_UPTAKE	2	3.389831	1.95500000	0.10711864	0.042280702
DIFF_LIVER_UPTAKE	1	1.694915	-1.59000000	0.01779661	0.045517241
DIFF_ESTIMATED_SPLEEN_VOL	9	15.254237	-15.83333333	8.61694915	13.018000000
TIME_BETWEE_PET	4	6.779661	30.25000000	9.62711864	8.127272727
dias	10	16.949153	293.80000000	57.15254237	8.857142857
DIFF_SLR	1	1.694915	0.68874973	0.00266275	-0.009166336
DIFF_BMLR	3	5.084746	-0.07330307	0.02945905	0.034964164
OVERALL_TIME	5	8.474576	57.80000000	19.10169492	15.518518519

# Plot analisis de outliers:
plot_outlier(datos)

# Plot de normalidad:
plot_normality(datos)

Correlacion variables cuantitativas

vec_var_num = c('CYCLES_BETWEEN_PET1_PET2',
                'AGE',
                'DIFF_WBC',
                'DIFF_RBC',
                'DIFF_HB',
                'DIFF_PLT',
                'DIFF_CRP',
                'DIFF_ALBUMIN',
                'DIFF_LDH',
                'DIFF_eGFR',
                'DIFF_AST',
                'DIFF_ALT',
                'DIFF_K',
                'DIFF_BGL',
                'BMI',
                'DIFF_BW',
                'DIFF_SPLEEN_UPTAKE',
                'DIFF_BM_UPTAKE',
                'DIFF_LIVER_UPTAKE',
                'DIFF_ESTIMATED_SPLEEN_VOL',
                'TIME_BETWEE_PET',
                'dias',
                'DIFF_SLR',
                'DIFF_BMLR',
                'OVERALL_TIME')

options(repr.plot.width=16, repr.plot.height=10)

plot_correlate(datos[,vec_var_num],method = "spearman")

# coeficiente de correlación
library(Hmisc)
vec_var_num = c('CYCLES_BETWEEN_PET1_PET2',
                'AGE',
                'DIFF_WBC',
                'DIFF_RBC',
                'DIFF_HB',
                'DIFF_PLT',
                'DIFF_CRP',
                'DIFF_ALBUMIN',
                'DIFF_LDH',
                'DIFF_eGFR',
                'DIFF_AST',
                'DIFF_ALT',
                'DIFF_K',
                'DIFF_BGL',
                'BMI',
                'DIFF_BW',
                'DIFF_SPLEEN_UPTAKE',
                'DIFF_BM_UPTAKE',
                'DIFF_LIVER_UPTAKE',
                'DIFF_ESTIMATED_SPLEEN_VOL',
                'TIME_BETWEE_PET',
                'dias',
                'DIFF_SLR',
                'DIFF_BMLR',
                'OVERALL_TIME')

# Correlaciones por parejas:

flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    row = rownames(cormat)[row(cormat)[ut]],
    column = rownames(cormat)[col(cormat)[ut]],
    cor  =(cormat)[ut],
    p = pmat[ut]
  )
}


# Calculamos la matriz de correlacion y visualizamos la correlacion de mayor intensidad
tcor<-rcorr(as.matrix(datos[,vec_var_num]),type = "spearman")
corr_data <- flattenCorrMatrix(tcor$r, tcor$P) %>%
    arrange(desc(abs(cor))) #%>%
corr_data
options(warn = -1)

Loading required package: survival


Attaching package: 'survival'


The following object is masked from 'package:caret':

    cluster


Loading required package: Formula


Attaching package: 'Hmisc'


The following object is masked from 'package:dlookr':

    describe


The following objects are masked from 'package:dplyr':

    src, summarize


The following object is masked from 'package:plotly':

    subplot


The following objects are masked from 'package:base':

    format.pval, units

A data.frame: 300 × 4

row	column	cor	p
<chr>	<chr>	<dbl>	<dbl>
DIFF_AST	DIFF_ALT	0.850032878	0.000000e+00
DIFF_RBC	DIFF_HB	0.651414630	2.309223e-08
DIFF_SLR	DIFF_BMLR	0.587655981	9.917998e-07
BMI	DIFF_BW	-0.491864226	7.608448e-05
DIFF_RBC	TIME_BETWEE_PET	0.456778390	2.763295e-04
CYCLES_BETWEEN_PET1_PET2	TIME_BETWEE_PET	0.405997284	1.420657e-03
DIFF_LDH	DIFF_AST	0.403741050	1.519114e-03
DIFF_LDH	DIFF_ALT	0.391021614	2.197503e-03
DIFF_SPLEEN_UPTAKE	DIFF_BMLR	0.361416383	4.916694e-03
DIFF_PLT	DIFF_ALBUMIN	-0.344376087	7.566172e-03
DIFF_WBC	DIFF_LIVER_UPTAKE	-0.341969139	8.026443e-03
DIFF_WBC	DIFF_RBC	0.334297738	9.660078e-03
DIFF_PLT	DIFF_LIVER_UPTAKE	-0.332169161	1.016160e-02
DIFF_CRP	DIFF_BGL	0.332110388	1.017576e-02
DIFF_HB	DIFF_BGL	-0.324202650	1.224414e-02
DIFF_CRP	DIFF_ALBUMIN	-0.312756662	1.587521e-02
DIFF_ESTIMATED_SPLEEN_VOL	DIFF_BMLR	-0.312219641	1.606611e-02
DIFF_WBC	TIME_BETWEE_PET	0.309196172	1.717791e-02
DIFF_HB	BMI	-0.308860281	1.730539e-02
DIFF_WBC	DIFF_PLT	0.306742460	1.812788e-02
DIFF_RBC	BMI	-0.301445125	2.033234e-02
BMI	DIFF_SPLEEN_UPTAKE	0.298415856	2.169234e-02
DIFF_K	DIFF_ESTIMATED_SPLEEN_VOL	-0.295751805	2.295131e-02
DIFF_RBC	DIFF_BGL	-0.285039905	2.865490e-02
DIFF_PLT	DIFF_eGFR	0.267531747	4.051234e-02
DIFF_RBC	DIFF_eGFR	-0.265528881	4.209569e-02
DIFF_ALBUMIN	DIFF_ESTIMATED_SPLEEN_VOL	-0.263249412	4.395935e-02
TIME_BETWEE_PET	OVERALL_TIME	-0.251015351	5.515436e-02
DIFF_K	BMI	-0.245995794	6.037113e-02
AGE	DIFF_K	0.243269865	6.336676e-02
DIFF_WBC	DIFF_CRP	0.240106391	6.699187e-02
DIFF_eGFR	DIFF_LIVER_UPTAKE	-0.239636454	6.754429e-02
CYCLES_BETWEEN_PET1_PET2	dias	0.232476215	7.642104e-02
DIFF_HB	DIFF_SLR	-0.232138423	7.686165e-02
CYCLES_BETWEEN_PET1_PET2	DIFF_RBC	0.231469664	7.773990e-02
BMI	OVERALL_TIME	-0.229712003	8.008611e-02
DIFF_eGFR	DIFF_ESTIMATED_SPLEEN_VOL	0.225456246	8.599905e-02
DIFF_PLT	DIFF_BW	0.224662286	8.713923e-02
DIFF_PLT	DIFF_BM_UPTAKE	0.218676019	9.612251e-02
CYCLES_BETWEEN_PET1_PET2	DIFF_SPLEEN_UPTAKE	0.218178907	9.689979e-02
CYCLES_BETWEEN_PET1_PET2	DIFF_BW	0.217188918	9.846228e-02
DIFF_eGFR	DIFF_K	-0.215671949	1.008944e-01
DIFF_CRP	DIFF_SLR	0.213549813	1.043747e-01
DIFF_SPLEEN_UPTAKE	DIFF_SLR	0.212471505	1.061782e-01
AGE	DIFF_ESTIMATED_SPLEEN_VOL	-0.212144856	1.067293e-01
DIFF_ALBUMIN	DIFF_SPLEEN_UPTAKE	-0.210972439	1.087254e-01
AGE	DIFF_BM_UPTAKE	-0.209992087	1.104165e-01
DIFF_ALBUMIN	DIFF_BW	-0.208511848	1.130081e-01
DIFF_PLT	DIFF_ESTIMATED_SPLEEN_VOL	0.208346729	1.133001e-01
DIFF_ESTIMATED_SPLEEN_VOL	dias	0.202249755	1.244907e-01
DIFF_BW	DIFF_SLR	-0.201912282	1.251337e-01
DIFF_LDH	dias	-0.201125812	1.266422e-01
DIFF_LDH	DIFF_SPLEEN_UPTAKE	-0.199789542	1.292366e-01
DIFF_BW	DIFF_BM_UPTAKE	-0.199406440	1.299878e-01
DIFF_BW	OVERALL_TIME	0.198338042	1.321001e-01
DIFF_CRP	DIFF_LIVER_UPTAKE	-0.197112295	1.345553e-01
DIFF_CRP	DIFF_SPLEEN_UPTAKE	0.195588945	1.376543e-01
DIFF_eGFR	DIFF_BMLR	-0.195067068	1.387282e-01
DIFF_CRP	DIFF_AST	-0.192540957	1.440151e-01
DIFF_SPLEEN_UPTAKE	DIFF_LIVER_UPTAKE	0.191941779	1.452910e-01
DIFF_AST	DIFF_LIVER_UPTAKE	0.190867246	1.476000e-01
DIFF_eGFR	dias	-0.187906878	1.541026e-01
DIFF_HB	DIFF_LDH	0.187568508	1.548592e-01
DIFF_BGL	DIFF_SPLEEN_UPTAKE	0.186031895	1.583293e-01
DIFF_RBC	DIFF_SLR	-0.184169382	1.626119e-01
DIFF_ALT	DIFF_LIVER_UPTAKE	0.183821381	1.634214e-01
DIFF_LIVER_UPTAKE	DIFF_ESTIMATED_SPLEEN_VOL	-0.182500950	1.665199e-01
DIFF_K	DIFF_BW	0.182231594	1.671572e-01
DIFF_HB	DIFF_CRP	-0.181204326	1.696041e-01
DIFF_ESTIMATED_SPLEEN_VOL	OVERALL_TIME	0.179380374	1.740129e-01
AGE	dias	-0.176621115	1.808401e-01
DIFF_LIVER_UPTAKE	OVERALL_TIME	-0.176475791	1.812049e-01
DIFF_SPLEEN_UPTAKE	DIFF_BM_UPTAKE	0.175052979	1.848053e-01
DIFF_LDH	DIFF_ESTIMATED_SPLEEN_VOL	0.174097293	1.872523e-01
DIFF_BW	DIFF_LIVER_UPTAKE	-0.172660843	1.909740e-01
AGE	DIFF_BGL	-0.171956240	1.928189e-01
BMI	DIFF_SLR	0.169934832	1.981820e-01
DIFF_AST	DIFF_SLR	-0.167937927	2.035837e-01
DIFF_WBC	DIFF_K	-0.165680215	2.098155e-01
DIFF_ALBUMIN	DIFF_LDH	-0.164825369	2.122098e-01
DIFF_ALBUMIN	DIFF_BM_UPTAKE	-0.162582380	2.185832e-01
DIFF_CRP	DIFF_ALT	-0.161585902	2.214572e-01
DIFF_BGL	DIFF_BM_UPTAKE	0.158184744	2.314647e-01
AGE	DIFF_BMLR	0.157801682	2.326111e-01
DIFF_HB	TIME_BETWEE_PET	0.156549779	2.363851e-01
DIFF_SPLEEN_UPTAKE	dias	0.154408799	2.429364e-01
AGE	DIFF_SPLEEN_UPTAKE	0.154006050	2.441826e-01
DIFF_HB	DIFF_BMLR	-0.152201553	2.498195e-01
DIFF_LDH	DIFF_BMLR	-0.151946458	2.506234e-01
DIFF_ALT	DIFF_ESTIMATED_SPLEEN_VOL	-0.151494784	2.520512e-01
CYCLES_BETWEEN_PET1_PET2	DIFF_AST	0.149772343	2.575466e-01
DIFF_BMLR	OVERALL_TIME	0.148252705	2.624617e-01
DIFF_BM_UPTAKE	DIFF_BMLR	0.148199021	2.626365e-01
DIFF_BGL	DIFF_SLR	0.146763519	2.673391e-01
DIFF_RBC	DIFF_PLT	0.145823592	2.704486e-01
CYCLES_BETWEEN_PET1_PET2	DIFF_HB	0.145484862	2.715751e-01
DIFF_HB	DIFF_BW	0.144768406	2.739680e-01
DIFF_ALT	DIFF_SPLEEN_UPTAKE	-0.141742296	2.842295e-01
CYCLES_BETWEEN_PET1_PET2	DIFF_WBC	0.141695974	2.843886e-01
DIFF_K	DIFF_BMLR	0.141082599	2.864997e-01
DIFF_AST	DIFF_BW	0.140893891	2.871513e-01
DIFF_BM_UPTAKE	DIFF_ESTIMATED_SPLEEN_VOL	0.138932726	2.939806e-01
AGE	DIFF_eGFR	-0.137964927	2.973895e-01
DIFF_ALT	DIFF_BGL	0.137455792	2.991930e-01
DIFF_RBC	DIFF_ALBUMIN	0.137195879	3.001165e-01
DIFF_BM_UPTAKE	DIFF_LIVER_UPTAKE	0.135970424	3.044954e-01
DIFF_LDH	DIFF_BM_UPTAKE	0.134972015	3.080934e-01
DIFF_PLT	OVERALL_TIME	0.133347983	3.140042e-01
DIFF_WBC	DIFF_AST	-0.133235917	3.144148e-01
DIFF_PLT	DIFF_BGL	0.132830543	3.159027e-01
DIFF_HB	dias	-0.132626234	3.166543e-01
DIFF_BW	TIME_BETWEE_PET	-0.132082056	3.186617e-01
DIFF_AST	DIFF_BMLR	-0.131378222	3.212701e-01
AGE	DIFF_BW	0.129681968	3.276121e-01
BMI	DIFF_BM_UPTAKE	0.128559948	3.318502e-01
DIFF_eGFR	DIFF_SPLEEN_UPTAKE	-0.128310050	3.327988e-01
DIFF_PLT	DIFF_LDH	0.125389101	3.440128e-01
DIFF_ALT	BMI	0.124347940	3.480660e-01
DIFF_ALT	DIFF_SLR	-0.122711399	3.544966e-01
DIFF_ALT	dias	-0.120804643	3.620803e-01
CYCLES_BETWEEN_PET1_PET2	BMI	-0.120506857	3.632736e-01
CYCLES_BETWEEN_PET1_PET2	DIFF_LIVER_UPTAKE	0.118003853	3.733978e-01
TIME_BETWEE_PET	dias	0.117319793	3.761940e-01
DIFF_WBC	DIFF_eGFR	0.116837383	3.781735e-01
DIFF_ALBUMIN	DIFF_BGL	-0.116747784	3.785418e-01
BMI	DIFF_BMLR	0.116075864	3.813109e-01
DIFF_BGL	DIFF_BMLR	-0.115896704	3.820512e-01
DIFF_WBC	dias	-0.115709055	3.828276e-01
TIME_BETWEE_PET	DIFF_SLR	-0.115694215	3.828891e-01
AGE	DIFF_RBC	0.114194672	3.891276e-01
DIFF_eGFR	DIFF_BGL	0.114025988	3.898332e-01
DIFF_K	DIFF_BM_UPTAKE	0.111859701	3.989608e-01
BMI	DIFF_LIVER_UPTAKE	0.111109488	4.021508e-01
DIFF_RBC	dias	0.110064711	4.066180e-01
DIFF_HB	DIFF_LIVER_UPTAKE	0.109662712	4.083445e-01
DIFF_RBC	OVERALL_TIME	0.109593674	4.086415e-01
DIFF_RBC	DIFF_ESTIMATED_SPLEEN_VOL	-0.109325229	4.097972e-01
DIFF_BW	DIFF_BMLR	-0.108083231	4.151691e-01
DIFF_HB	DIFF_PLT	0.108061499	4.152635e-01
DIFF_CRP	OVERALL_TIME	0.106974177	4.200000e-01
DIFF_eGFR	DIFF_ALT	0.106316758	4.228788e-01
DIFF_RBC	DIFF_BW	0.105614050	4.259682e-01
DIFF_LDH	DIFF_K	0.105609307	4.259891e-01
DIFF_CRP	DIFF_BMLR	0.103917930	4.334778e-01
DIFF_ALBUMIN	DIFF_eGFR	-0.100619557	4.482927e-01
DIFF_PLT	DIFF_CRP	0.100242605	4.500035e-01
dias	DIFF_BMLR	0.098758901	4.567719e-01
DIFF_ALBUMIN	DIFF_ALT	0.098049244	4.600288e-01
dias	DIFF_SLR	0.096313054	4.680500e-01
AGE	DIFF_PLT	0.095504278	4.718122e-01
DIFF_CRP	dias	0.093189609	4.826683e-01
DIFF_HB	DIFF_ALBUMIN	0.092521958	4.858240e-01
DIFF_WBC	DIFF_BMLR	-0.092347707	4.866494e-01
BMI	dias	0.091468396	4.908258e-01
AGE	DIFF_ALT	-0.091036824	4.928824e-01
DIFF_CRP	DIFF_BW	0.090770858	4.941521e-01
DIFF_eGFR	OVERALL_TIME	-0.090685795	4.945586e-01
DIFF_LDH	DIFF_LIVER_UPTAKE	0.089550058	5.000018e-01
DIFF_ALT	DIFF_BMLR	-0.089542207	5.000396e-01
TIME_BETWEE_PET	DIFF_BMLR	-0.089388757	5.007774e-01
DIFF_BM_UPTAKE	OVERALL_TIME	0.089095900	5.021871e-01
DIFF_AST	DIFF_BM_UPTAKE	0.087808513	5.084083e-01
DIFF_PLT	DIFF_SPLEEN_UPTAKE	0.086549845	5.145285e-01
CYCLES_BETWEEN_PET1_PET2	AGE	-0.086042255	5.170072e-01
CYCLES_BETWEEN_PET1_PET2	DIFF_LDH	0.085330284	5.204939e-01
DIFF_WBC	DIFF_ESTIMATED_SPLEEN_VOL	0.083082498	5.315790e-01
DIFF_LDH	OVERALL_TIME	0.082870278	5.326315e-01
DIFF_eGFR	DIFF_BW	0.082630383	5.338226e-01
DIFF_SPLEEN_UPTAKE	OVERALL_TIME	0.081705190	5.384283e-01
CYCLES_BETWEEN_PET1_PET2	DIFF_ALT	0.081684775	5.385302e-01
DIFF_BW	DIFF_ESTIMATED_SPLEEN_VOL	0.080582452	5.440434e-01
DIFF_AST	DIFF_ESTIMATED_SPLEEN_VOL	-0.080541228	5.442501e-01
DIFF_HB	DIFF_eGFR	-0.079834574	5.477993e-01
DIFF_WBC	DIFF_ALT	-0.079767958	5.481345e-01
DIFF_CRP	DIFF_K	-0.077960800	5.572641e-01
DIFF_AST	DIFF_BGL	0.077169292	5.612853e-01
DIFF_ALBUMIN	DIFF_LIVER_UPTAKE	0.076466720	5.648661e-01
DIFF_ALBUMIN	DIFF_SLR	-0.075968788	5.674104e-01
DIFF_BM_UPTAKE	dias	0.075589384	5.693526e-01
BMI	DIFF_ESTIMATED_SPLEEN_VOL	0.074022122	5.774081e-01
DIFF_AST	OVERALL_TIME	0.072922167	5.830927e-01
DIFF_PLT	BMI	-0.070594596	5.952046e-01
DIFF_HB	DIFF_K	0.070360572	5.964286e-01
DIFF_K	DIFF_SLR	-0.069795991	5.993859e-01
DIFF_K	TIME_BETWEE_PET	-0.069787035	5.994329e-01
DIFF_ESTIMATED_SPLEEN_VOL	DIFF_SLR	-0.069375648	6.015919e-01
AGE	DIFF_HB	-0.068925698	6.039573e-01
AGE	DIFF_CRP	-0.068398989	6.067312e-01
DIFF_BGL	OVERALL_TIME	-0.067583189	6.110387e-01
DIFF_LDH	DIFF_BW	0.067387974	6.120714e-01
DIFF_PLT	DIFF_AST	0.066987184	6.141939e-01
DIFF_LIVER_UPTAKE	DIFF_SLR	0.065637009	6.213673e-01
DIFF_AST	DIFF_SPLEEN_UPTAKE	-0.065194517	6.237260e-01
DIFF_eGFR	TIME_BETWEE_PET	-0.063390880	6.333787e-01
DIFF_ALBUMIN	TIME_BETWEE_PET	0.063158379	6.346275e-01
DIFF_CRP	DIFF_ESTIMATED_SPLEEN_VOL	0.062984072	6.355644e-01
DIFF_K	OVERALL_TIME	-0.062138062	6.401196e-01
DIFF_SPLEEN_UPTAKE	TIME_BETWEE_PET	-0.061730178	6.423206e-01
DIFF_HB	DIFF_SPLEEN_UPTAKE	-0.061064909	6.459169e-01
DIFF_RBC	DIFF_BM_UPTAKE	-0.060671596	6.480469e-01
DIFF_WBC	DIFF_HB	0.060633075	6.482557e-01
DIFF_WBC	BMI	0.060230577	6.504386e-01
DIFF_WBC	DIFF_ALBUMIN	-0.059910864	6.521746e-01
DIFF_RBC	DIFF_LIVER_UPTAKE	-0.059690806	6.533706e-01
DIFF_eGFR	DIFF_AST	0.057542814	6.650898e-01
CYCLES_BETWEEN_PET1_PET2	DIFF_BM_UPTAKE	-0.057346038	6.661674e-01
DIFF_PLT	DIFF_K	0.055419961	6.767505e-01
DIFF_RBC	DIFF_LDH	0.054419781	6.822710e-01
DIFF_ALT	DIFF_BM_UPTAKE	0.053657539	6.864893e-01
DIFF_RBC	DIFF_BMLR	-0.052749244	6.915283e-01
DIFF_PLT	DIFF_SLR	0.052109363	6.950862e-01
DIFF_BW	dias	-0.051296450	6.996157e-01
CYCLES_BETWEEN_PET1_PET2	DIFF_BGL	0.050768693	7.025619e-01
CYCLES_BETWEEN_PET1_PET2	DIFF_CRP	0.050734770	7.027514e-01
DIFF_WBC	DIFF_BW	-0.050526323	7.039164e-01
CYCLES_BETWEEN_PET1_PET2	DIFF_ALBUMIN	-0.049830798	7.078084e-01
CYCLES_BETWEEN_PET1_PET2	DIFF_K	-0.047788342	7.192803e-01
DIFF_HB	DIFF_ALT	0.047787456	7.192853e-01
DIFF_WBC	DIFF_SLR	0.047401260	7.214615e-01
DIFF_RBC	DIFF_AST	0.046379776	7.272280e-01
DIFF_BGL	DIFF_LIVER_UPTAKE	0.045701676	7.310644e-01
DIFF_PLT	dias	0.045659141	7.313053e-01
DIFF_LDH	DIFF_eGFR	0.043444587	7.438808e-01
DIFF_PLT	TIME_BETWEE_PET	0.043035123	7.462134e-01
CYCLES_BETWEEN_PET1_PET2	DIFF_BMLR	-0.042477230	7.493951e-01
DIFF_HB	DIFF_AST	0.041854824	7.529498e-01
DIFF_CRP	DIFF_eGFR	0.041122916	7.571363e-01
DIFF_RBC	DIFF_CRP	-0.040889695	7.584718e-01
DIFF_BM_UPTAKE	DIFF_SLR	0.040651713	7.598352e-01
DIFF_PLT	DIFF_BMLR	0.040185291	7.625096e-01
DIFF_WBC	OVERALL_TIME	0.038778175	7.705944e-01
AGE	DIFF_SLR	-0.038722634	7.709141e-01
DIFF_RBC	DIFF_K	-0.037850556	7.759376e-01
DIFF_LDH	DIFF_BGL	-0.037709926	7.767485e-01
CYCLES_BETWEEN_PET1_PET2	OVERALL_TIME	0.037361386	7.787594e-01
DIFF_ALBUMIN	BMI	0.037348764	7.788323e-01
DIFF_RBC	DIFF_SPLEEN_UPTAKE	-0.036725270	7.824331e-01
DIFF_AST	BMI	0.036238237	7.852491e-01
DIFF_WBC	DIFF_SPLEEN_UPTAKE	-0.034533152	7.951290e-01
DIFF_CRP	DIFF_LDH	0.034520556	7.952021e-01
DIFF_LDH	BMI	-0.034121464	7.975193e-01
DIFF_LIVER_UPTAKE	TIME_BETWEE_PET	0.033922233	7.986768e-01
CYCLES_BETWEEN_PET1_PET2	DIFF_eGFR	-0.033121337	8.033339e-01
DIFF_LIVER_UPTAKE	dias	-0.033019413	8.039271e-01
BMI	TIME_BETWEE_PET	0.032398481	8.075431e-01
DIFF_eGFR	DIFF_BM_UPTAKE	0.031124425	8.149751e-01
DIFF_BM_UPTAKE	TIME_BETWEE_PET	0.031096289	8.151394e-01
DIFF_HB	DIFF_ESTIMATED_SPLEEN_VOL	-0.030337572	8.195732e-01
DIFF_BGL	BMI	0.030209141	8.203243e-01
DIFF_LDH	TIME_BETWEE_PET	0.029999054	8.215533e-01
DIFF_HB	DIFF_BM_UPTAKE	-0.029594014	8.239239e-01
DIFF_eGFR	BMI	0.029515766	8.243821e-01
DIFF_WBC	DIFF_LDH	-0.028437404	8.307019e-01
AGE	DIFF_LIVER_UPTAKE	0.028000077	8.332679e-01
AGE	OVERALL_TIME	0.026043042	8.447718e-01
CYCLES_BETWEEN_PET1_PET2	DIFF_ESTIMATED_SPLEEN_VOL	-0.025724648	8.466465e-01
DIFF_ALBUMIN	DIFF_K	-0.025545845	8.476997e-01
DIFF_PLT	DIFF_ALT	-0.025470540	8.481433e-01
DIFF_SPLEEN_UPTAKE	DIFF_ESTIMATED_SPLEEN_VOL	-0.025398589	8.485672e-01
DIFF_BGL	DIFF_ESTIMATED_SPLEEN_VOL	-0.024962002	8.511404e-01
DIFF_ALBUMIN	dias	0.023910418	8.573446e-01
DIFF_BGL	dias	0.023460185	8.600035e-01
DIFF_BW	DIFF_SPLEEN_UPTAKE	-0.022970861	8.628950e-01
DIFF_CRP	TIME_BETWEE_PET	-0.022441683	8.660241e-01
DIFF_AST	DIFF_K	0.021994564	8.686696e-01
dias	OVERALL_TIME	0.021761354	8.700499e-01
CYCLES_BETWEEN_PET1_PET2	DIFF_SLR	0.021614324	8.709204e-01
AGE	TIME_BETWEE_PET	0.020162783	8.795222e-01
DIFF_ESTIMATED_SPLEEN_VOL	TIME_BETWEE_PET	0.020094967	8.799244e-01
DIFF_CRP	DIFF_BM_UPTAKE	-0.019743957	8.820068e-01
DIFF_HB	OVERALL_TIME	0.019727705	8.821032e-01
DIFF_K	DIFF_SPLEEN_UPTAKE	0.019088544	8.858971e-01
DIFF_SLR	OVERALL_TIME	0.017590295	8.948001e-01
DIFF_ALT	DIFF_BW	-0.017573102	8.949023e-01
DIFF_K	DIFF_BGL	0.016663500	9.003139e-01
DIFF_AST	dias	0.016572664	9.008546e-01
DIFF_CRP	BMI	-0.016265034	9.026860e-01
DIFF_ALT	OVERALL_TIME	-0.015879750	9.049803e-01
DIFF_ALBUMIN	OVERALL_TIME	-0.015119615	9.095093e-01
AGE	BMI	0.014950711	9.105160e-01
DIFF_AST	TIME_BETWEE_PET	0.014266294	9.145968e-01
DIFF_LDH	DIFF_SLR	0.013268646	9.205491e-01
DIFF_ALBUMIN	DIFF_AST	0.013151358	9.212492e-01
DIFF_eGFR	DIFF_SLR	0.011981648	9.282341e-01
AGE	DIFF_LDH	0.010928728	9.345263e-01
AGE	DIFF_AST	0.010708650	9.358419e-01
DIFF_BGL	DIFF_BW	-0.010689323	9.359575e-01
DIFF_LIVER_UPTAKE	DIFF_BMLR	-0.010053041	9.397623e-01
DIFF_ALT	DIFF_K	0.009469946	9.432503e-01
DIFF_RBC	DIFF_ALT	0.008475072	9.492039e-01
AGE	DIFF_WBC	-0.008148403	9.511594e-01
DIFF_ALT	TIME_BETWEE_PET	0.007030515	9.578533e-01
DIFF_K	DIFF_LIVER_UPTAKE	0.005641048	9.661775e-01
DIFF_WBC	DIFF_BGL	-0.004691474	9.718684e-01
DIFF_ALBUMIN	DIFF_BMLR	0.003521538	9.788819e-01
DIFF_BGL	TIME_BETWEE_PET	0.002856731	9.828679e-01
AGE	DIFF_ALBUMIN	-0.002662533	9.840324e-01
CYCLES_BETWEEN_PET1_PET2	DIFF_PLT	-0.001665029	9.900142e-01
DIFF_WBC	DIFF_BM_UPTAKE	-0.001475875	9.911486e-01
DIFF_K	dias	0.001458314	9.912539e-01

Descripcion variables cualitativas de entrada

# Todas la tablas de frecuencias:
all_var <- univar_category(datos) 
all_var

$STATUS

A tibble: 2 × 3

STATUS	n	rate
<fct>	<int>	<dbl>
ALIVE	38	0.6440678
DEATH	21	0.3559322

$GENDER

A tibble: 2 × 3

GENDER	n	rate
<fct>	<int>	<dbl>
FEMALE	20	0.3389831
MALE	39	0.6610169

$TNM_STAGE

A tibble: 4 × 3

TNM_STAGE	n	rate
<fct>	<int>	<dbl>
I	6	0.1016949
II	15	0.2542373
III	26	0.4406780
IV	12	0.2033898

$DIAGNOSTIC

A tibble: 10 × 3

DIAGNOSTIC	n	rate
<fct>	<int>	<dbl>
EWING SARCOMA	2	0.03389831
GASTRIC CANCER	3	0.05084746
GINECOLOGICAL	7	0.11864407
HEAD AND NECK	10	0.16949153
LUNG CANCER	19	0.32203390
MELANOMA	5	0.08474576
PANCREAS CANCER	7	0.11864407
RENAL CANCER	4	0.06779661
SARCOMA	1	0.01694915
UROTHELIAL CARCINOMA	1	0.01694915

$TREATMENT

A tibble: 2 × 3

TREATMENT	n	rate
<fct>	<int>	<dbl>
CHEMO	37	0.6271186
ICI	22	0.3728814

$ECOGPS

A tibble: 5 × 3

ECOGPS	n	rate
<fct>	<int>	<dbl>
ASYMTOMATIC	26	0.44067797
BEDBOUND	1	0.01694915
SYMPTOMATIC >50 % IN THE BED	1	0.01694915
SYMPTOMATIC BUT AMBULATORY	17	0.28813559
SYMPTOMATIC,<50% IN BED DURING THE DAY	14	0.23728814

$COMORBIDITIES

A tibble: 6 × 3

COMORBIDITIES	n	rate
<fct>	<int>	<dbl>
ATEROESCLEROSIS	3	0.05084746
CANCER RRECURRENCE	8	0.13559322
CHRONIC INFLAMMATION	9	0.15254237
DIABETES MELLITUS	13	0.22033898
HYPERLIPIDEMIA	12	0.20338983
HYPERTENSION	14	0.23728814

$CTCNCI

A tibble: 5 × 3

CTCNCI	n	rate
<fct>	<int>	<dbl>
HIGHT SEVERE	4	0.06779661
MODERATE	11	0.18644068
NO SIDE EFFECTS	28	0.47457627
SEVERE	12	0.20338983
SLIGTHLY SIDE EFFCTS	4	0.06779661

$ACTION_TAKEN_

A tibble: 7 × 3

ACTION_TAKEN_	n	rate
<fct>	<int>	<dbl>
ADDED OTHER TREATMEN	3	0.05084746
ADDED STEROIDS	1	0.01694915
DOSE NOT CHANGED	21	0.35593220
DOSE REDUCED	17	0.28813559
DRUG INTERRUPTED	9	0.15254237
DRUG WIHDRAWN	1	0.01694915
UNKNOW	7	0.11864407

# Diagrama de barras por separado variables cualitativas:
plot(all_var)

# Todos los diagramas de barra juntos:
plot_bar_category(datos)

# Tabla de frecuencias + diagrama de barras + diagrama de sectores en una iteración:

vec_var_cual <- c('GENDER','TNM_STAGE','DIAGNOSTIC','TREATMENT','ECOGPS','COMORBIDITIES','CTCNCI','ACTION_TAKEN_')

for (i in vec_var_cual) {
    
    # 0. Seleccionar la variable con el contador i:
    #***************************************************************    
    var_i = i
    
    print("********************************************")
    print(var_i)
    print("********************************************")

    # 1. Creo la tabla de frecuencias relativas y absolutas:
    #***************************************************************

    # Tabla de frecuencias absolutas:
    tabla_frec = data.frame(t(table(datos[,var_i])))[,2:3]
    colnames(tabla_frec)<-c("Grupos","Frec_Absoluta")

    # Tabla de frecuencias relativas:
    tabla_frec$Frec_Relativa <- tabla_frec$Frec_Absoluta/sum(tabla_frec$Frec_Absoluta)
    print(tabla_frec)


    # 3. Crear el diagrama de sectores
    #***************************************************************

    data <- data.frame(
      group=tabla_frec$Grupos,
      value=tabla_frec$Frec_Relativa
    )

    
    data <- data %>% 
      arrange(desc(group)) %>%
      mutate(prop = value / sum(data$value) *100) %>%
      mutate(ypos = cumsum(prop)- 0.5*prop )

   
    p2 <- ggplot(data, aes(x="", y=prop, fill=group)) +
      geom_bar(stat="identity", width=1, color="white") +
      coord_polar("y", start=0) +
      theme_void() + 
      theme(legend.position="none") +

      geom_text(aes(y = ypos, label = group), color = "white", size=6) +
      scale_fill_brewer(palette="Set1")

    
    
    # 2. Crear el barplot frecuencia Absoluta
    #***************************************************************
    p1 <- ggplot(data=tabla_frec, aes(x=Grupos, y=Frec_Absoluta)) +
      geom_bar(stat="identity", fill="steelblue")+
      geom_text(aes(label=Frec_Relativa), vjust=1.6, color="black", size=5)+
      theme_minimal()

   
      grid.arrange(p1, p2,
               nrow=2, ncol = 1,
               top = textGrob(var_i,gp=gpar(fontsize=16,font=3)))    
    
    
}

[1] "********************************************"
[1] "GENDER"
[1] "********************************************"
  Grupos Frec_Absoluta Frec_Relativa
1 FEMALE            20     0.3389831
2   MALE            39     0.6610169
[1] "********************************************"
[1] "TNM_STAGE"
[1] "********************************************"
  Grupos Frec_Absoluta Frec_Relativa
1      I             6     0.1016949
2     II            15     0.2542373
3    III            26     0.4406780
4     IV            12     0.2033898

[1] "********************************************"
[1] "DIAGNOSTIC"
[1] "********************************************"
                 Grupos Frec_Absoluta Frec_Relativa
1         EWING SARCOMA             2    0.03389831
2        GASTRIC CANCER             3    0.05084746
3         GINECOLOGICAL             7    0.11864407
4         HEAD AND NECK            10    0.16949153
5           LUNG CANCER            19    0.32203390
6              MELANOMA             5    0.08474576
7       PANCREAS CANCER             7    0.11864407
8          RENAL CANCER             4    0.06779661
9               SARCOMA             1    0.01694915
10 UROTHELIAL CARCINOMA             1    0.01694915

[1] "********************************************"
[1] "TREATMENT"
[1] "********************************************"
  Grupos Frec_Absoluta Frec_Relativa
1  CHEMO            37     0.6271186
2    ICI            22     0.3728814

[1] "********************************************"
[1] "ECOGPS"
[1] "********************************************"
                                  Grupos Frec_Absoluta Frec_Relativa
1                            ASYMTOMATIC            26    0.44067797
2                               BEDBOUND             1    0.01694915
3           SYMPTOMATIC >50 % IN THE BED             1    0.01694915
4             SYMPTOMATIC BUT AMBULATORY            17    0.28813559
5 SYMPTOMATIC,<50% IN BED DURING THE DAY            14    0.23728814

[1] "********************************************"
[1] "COMORBIDITIES"
[1] "********************************************"
                Grupos Frec_Absoluta Frec_Relativa
1      ATEROESCLEROSIS             3    0.05084746
2   CANCER RRECURRENCE             8    0.13559322
3 CHRONIC INFLAMMATION             9    0.15254237
4    DIABETES MELLITUS            13    0.22033898
5       HYPERLIPIDEMIA            12    0.20338983
6         HYPERTENSION            14    0.23728814

[1] "********************************************"
[1] "CTCNCI"
[1] "********************************************"
                Grupos Frec_Absoluta Frec_Relativa
1         HIGHT SEVERE             4    0.06779661
2             MODERATE            11    0.18644068
3      NO SIDE EFFECTS            28    0.47457627
4               SEVERE            12    0.20338983
5 SLIGTHLY SIDE EFFCTS             4    0.06779661

[1] "********************************************"
[1] "ACTION_TAKEN_"
[1] "********************************************"
                Grupos Frec_Absoluta Frec_Relativa
1 ADDED OTHER TREATMEN             3    0.05084746
2       ADDED STEROIDS             1    0.01694915
3     DOSE NOT CHANGED            21    0.35593220
4         DOSE REDUCED            17    0.28813559
5     DRUG INTERRUPTED             9    0.15254237
6        DRUG WIHDRAWN             1    0.01694915
7               UNKNOW             7    0.11864407

EXPLORACION MULTIVARIADA DEL DATASET

Variables numericas vs salida

Estadisticos de las variables numericas vs salida

# DESCRIPCION MULTIVARIADA: las variables en función de la respuesta
name_y <- as.name(respuesta[1])

descripcion_num <- datos %>%
  group_by(!!name_y)%>%
  dlookr::describe()
descripcion_num

A tibble: 50 × 27

described_variables	STATUS	n	na	mean	sd	se_mean	IQR	skewness	kurtosis	p00	p01	p05	p10	p20	p25	p30	p40	p50	p60	p70	p75	p80	p90	p95	p99	p100
<chr>	<fct>	<int>	<int>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
AGE	ALIVE	38	0	68.552631579	8.1562358	1.32311616	12.2500000	-0.35796368	-0.48367838	52.0000000	52.7400000	54.0000000	55.7000000	60.40000000	62.50000000	65.00000000	68.000000000	70.000000000	72.00000000	74.0000000	74.7500000	75.6000000	76.3000000	79.0000000	83.4100000	86.0000000
AGE	DEATH	21	0	70.904761905	9.4228698	2.05623878	14.0000000	-0.14495828	-0.82993240	53.0000000	53.6000000	56.0000000	60.0000000	63.00000000	64.00000000	67.00000000	68.000000000	70.000000000	74.00000000	77.0000000	78.0000000	79.0000000	83.0000000	83.0000000	86.2000000	87.0000000
BMI	ALIVE	38	0	22.135789474	4.2896479	0.69587278	5.3525000	0.88974456	1.40256997	15.4300000	15.7297000	16.4355000	17.1050000	18.22400000	18.83250000	20.12300000	20.808000000	21.825000000	22.83200000	23.5790000	24.1850000	25.7080000	26.9140000	29.1775000	33.4975000	35.8100000
BMI	DEATH	21	0	20.474761905	3.3661545	0.73455514	5.6200000	-0.05915208	-1.07758686	14.0800000	14.4980000	16.1700000	16.6800000	17.30000000	17.89000000	18.07000000	19.260000000	20.070000000	21.79000000	23.1400000	23.5100000	23.7600000	24.4600000	25.2100000	25.6660000	25.7800000
CYCLES_BETWEEN_PET1_PET2	ALIVE	38	0	7.368421053	8.8330493	1.43290981	3.7500000	3.23904673	12.32718502	2.0000000	2.0000000	2.0000000	2.0000000	3.00000000	3.00000000	3.00000000	4.000000000	4.000000000	5.00000000	6.0000000	6.7500000	11.2000000	14.0000000	21.3500000	40.9700000	48.0000000
CYCLES_BETWEEN_PET1_PET2	DEATH	21	0	6.523809524	4.0201872	0.87727676	4.0000000	1.58544523	2.70180500	2.0000000	2.0000000	2.0000000	3.0000000	4.00000000	4.00000000	4.00000000	5.000000000	6.000000000	6.00000000	7.0000000	8.0000000	9.0000000	10.0000000	15.0000000	17.4000000	18.0000000
dias	ALIVE	38	0	46.842105263	137.8935207	22.36928289	23.7500000	4.06563184	17.78192579	0.0000000	0.0000000	0.0000000	0.0000000	0.00000000	0.00000000	0.00000000	0.000000000	0.000000000	2.00000000	14.9000000	23.7500000	30.4000000	64.2000000	319.6500000	597.3600000	735.0000000
dias	DEATH	21	0	75.809523810	127.5623844	27.83639439	86.0000000	1.95190402	3.08842106	0.0000000	0.0000000	0.0000000	0.0000000	0.00000000	0.00000000	0.00000000	3.000000000	7.000000000	33.00000000	52.0000000	86.0000000	114.0000000	233.0000000	364.0000000	425.6000000	441.0000000
DIFF_ALBUMIN	ALIVE	38	0	-0.057631579	0.6964948	0.11298638	0.4625000	0.30469934	1.31440247	-1.7000000	-1.6260000	-1.2620000	-0.6430000	-0.48400000	-0.29500000	-0.23700000	-0.168000000	-0.120000000	-0.06600000	0.0500000	0.1675000	0.2200000	1.0310000	1.2025000	1.5883000	1.7400000
DIFF_ALBUMIN	DEATH	21	0	-0.057619048	0.9679251	0.21121858	0.2500000	0.88964699	4.51502526	-2.2800000	-2.0940000	-1.3500000	-0.9000000	-0.37000000	-0.17000000	-0.16000000	-0.110000000	-0.060000000	-0.05000000	0.0500000	0.0800000	0.1000000	0.2000000	1.5700000	2.5780000	2.8300000
DIFF_ALT	ALIVE	38	0	-5.223684211	15.9310412	2.58435614	10.2250000	-3.38559938	16.87827941	-84.7000000	-62.2040000	-19.9900000	-15.1500000	-10.08000000	-9.20000000	-6.67000000	-4.640000000	-2.250000000	-1.10000000	0.5500000	1.0250000	2.0400000	5.2300000	13.7400000	17.7450000	18.3000000
DIFF_ALT	DEATH	21	0	-17.208571429	57.1424671	12.46950861	9.4200000	-3.82284716	15.51173073	-250.8000000	-217.6200000	-84.9000000	-16.9000000	-9.20000000	-8.80000000	-8.60000000	-6.000000000	-2.600000000	0.00000000	0.4000000	0.6200000	3.3000000	7.5000000	14.3000000	18.3800000	19.4000000
DIFF_AST	ALIVE	38	0	-4.778947368	19.2680124	3.12568435	9.0250000	-4.78306274	26.77840367	-111.5000000	-77.5340000	-18.8500000	-12.6100000	-7.14000000	-6.55000000	-5.51000000	-3.120000000	-0.700000000	0.02000000	1.1400000	2.4750000	4.3600000	5.0600000	7.3050000	14.8300000	18.9000000
DIFF_AST	DEATH	21	0	-11.800476190	49.7210238	10.85001691	12.0000000	-4.03400147	17.16036256	-219.6000000	-186.8600000	-55.9000000	-11.6000000	-5.80000000	-5.50000000	-3.50000000	-1.100000000	-0.800000000	0.00000000	1.7000000	6.5000000	6.6000000	11.9000000	13.2000000	15.1200000	15.6000000
DIFF_BGL	ALIVE	38	0	-3.315789474	43.2723026	7.01969443	27.5000000	-1.63834398	4.74218790	-160.0000000	-144.4600000	-77.2000000	-45.5000000	-13.60000000	-12.75000000	-10.70000000	-4.600000000	2.000000000	9.40000000	12.0000000	14.7500000	19.6000000	30.4000000	64.0000000	67.7800000	70.0000000
DIFF_BGL	DEATH	21	0	2.428571429	22.6728283	4.94761676	23.0000000	-1.62777362	4.23786443	-70.0000000	-61.4000000	-27.0000000	-18.0000000	-6.00000000	-6.00000000	-2.00000000	-1.000000000	1.000000000	14.00000000	16.0000000	17.0000000	17.0000000	29.0000000	29.0000000	29.8000000	30.0000000
DIFF_BM_UPTAKE	ALIVE	38	0	0.100000000	0.6192498	0.10045558	0.7750000	0.47868759	1.70780206	-1.2000000	-1.1297000	-0.8315000	-0.6480000	-0.34800000	-0.28000000	-0.19800000	-0.028000000	0.095000000	0.20200000	0.4390000	0.4950000	0.5740000	0.7210000	0.8490000	1.6434000	2.0800000
DIFF_BM_UPTAKE	DEATH	21	0	0.120000000	0.5834724	0.12732411	0.6000000	0.97872356	2.56877396	-0.8200000	-0.8000000	-0.7200000	-0.4500000	-0.40000000	-0.21000000	-0.19000000	0.010000000	0.200000000	0.24000000	0.3500000	0.3900000	0.4100000	0.7000000	0.7400000	1.6120000	1.8300000
DIFF_BMLR	ALIVE	38	0	0.005877421	0.2736999	0.04439999	0.3242798	0.06548622	0.10607508	-0.6192344	-0.5805216	-0.3880261	-0.3188116	-0.20431746	-0.16686314	-0.13508456	-0.064767496	0.006350011	0.07472226	0.1402702	0.1574167	0.1849698	0.3389879	0.4989430	0.5797461	0.6197762
DIFF_BMLR	DEATH	21	0	0.072130571	0.3516416	0.07673450	0.3824610	0.42492308	1.98629788	-0.6207071	-0.6042323	-0.5383333	-0.3211071	-0.09719522	-0.09373885	-0.07072829	0.002627451	0.081944444	0.09726844	0.2001239	0.2887222	0.2893665	0.3768175	0.4389387	0.9038135	1.0200322
DIFF_BW	ALIVE	38	0	0.142105263	6.2442030	1.01294348	6.6500000	0.72112651	0.63190779	-11.1000000	-10.9890000	-8.5050000	-6.3200000	-4.86000000	-3.60000000	-2.38000000	-1.300000000	-0.550000000	0.00000000	0.8000000	3.0500000	4.5600000	9.0200000	12.6350000	15.4150000	15.6000000
DIFF_BW	DEATH	21	0	8.172380952	11.3566201	2.47821767	4.4000000	1.94716942	4.16313988	-4.3000000	-3.9000000	-2.3000000	-1.5000000	0.00000000	3.10000000	3.60000000	4.600000000	5.300000000	5.90000000	6.9000000	7.5000000	8.7000000	24.1000000	26.2000000	40.4560000	44.0200000
DIFF_CRP	ALIVE	38	0	0.586578947	4.5745998	0.74209809	0.4450000	2.28771118	7.20120744	-7.2300000	-6.9155000	-4.2550000	-3.0580000	-0.52000000	-0.33750000	-0.10000000	-0.052000000	-0.010000000	0.02000000	0.0400000	0.1075000	0.1800000	4.6960000	8.6990000	16.8606000	17.8300000
DIFF_CRP	DEATH	21	0	-0.123809524	4.1593082	0.90763546	1.1800000	-2.65479802	11.12447049	-15.8800000	-13.2320000	-2.6400000	-2.0300000	-0.22000000	-0.15000000	-0.02000000	0.030000000	0.250000000	0.32000000	0.3900000	1.0300000	1.1900000	2.9600000	3.0200000	6.3720000	7.2100000
DIFF_eGFR	ALIVE	38	0	-1.621052632	15.5613374	2.52438226	14.5000000	1.01248285	2.01317072	-30.1000000	-28.2500000	-24.2500000	-17.3400000	-12.98000000	-10.22500000	-9.38000000	-7.820000000	-3.150000000	0.08000000	2.8900000	4.2750000	8.0200000	14.9400000	25.2900000	42.7420000	47.7000000
DIFF_eGFR	DEATH	21	0	-4.085714286	18.1665430	3.96426469	17.0000000	0.37991024	1.92431183	-39.2000000	-37.9000000	-32.7000000	-26.4000000	-19.60000000	-10.20000000	-6.30000000	-4.600000000	-2.600000000	-0.90000000	3.8000000	6.8000000	7.5000000	9.5000000	9.9000000	38.2200000	45.3000000
DIFF_ESTIMATED_SPLEEN_VOL	ALIVE	38	0	6.955263158	55.4416796	8.99382805	46.5750000	0.38060194	1.62630342	-110.2000000	-101.8380000	-85.3900000	-63.1900000	-42.22000000	-15.85000000	-1.72000000	3.800000000	8.600000000	18.20000000	26.2600000	30.7250000	38.3600000	59.4800000	98.8900000	148.2880000	177.0000000
DIFF_ESTIMATED_SPLEEN_VOL	DEATH	21	0	11.623809524	39.4728502	8.61368210	37.1000000	-0.72159619	1.29425090	-85.5000000	-82.1000000	-68.5000000	-20.2000000	-5.60000000	-3.10000000	-2.40000000	2.400000000	15.100000000	24.00000000	33.1000000	34.0000000	34.2000000	55.2000000	65.2000000	80.4000000	84.2000000
DIFF_HB	ALIVE	38	0	0.169473684	2.1120644	0.34262208	2.8250000	0.51856836	0.08700801	-3.5000000	-3.3890000	-2.6900000	-2.2300000	-1.82000000	-1.45000000	-0.89000000	-0.420000000	-0.100000000	0.44000000	1.1800000	1.3750000	1.7600000	2.8820000	3.7150000	5.0600000	5.8000000
DIFF_HB	DEATH	21	0	-0.576190476	2.1030703	0.45892757	2.1000000	0.04091279	0.50400745	-5.2000000	-4.8600000	-3.5000000	-2.8000000	-1.60000000	-1.50000000	-1.20000000	-1.100000000	-0.900000000	-0.60000000	0.4000000	0.6000000	0.9000000	1.9000000	2.4000000	3.6800000	4.0000000
DIFF_K	ALIVE	38	0	0.107894737	0.3612004	0.05859444	0.4125000	0.14853719	-0.08333286	-0.6700000	-0.6293000	-0.4835000	-0.2810000	-0.18200000	-0.10000000	-0.06600000	0.048000000	0.075000000	0.14000000	0.2390000	0.3125000	0.3600000	0.6170000	0.7345000	0.8356000	0.8800000
DIFF_K	DEATH	21	0	0.220476190	0.4896067	0.10684095	0.5100000	-0.52745476	0.22208083	-0.9000000	-0.8480000	-0.6400000	-0.3400000	-0.01000000	0.00000000	0.00000000	0.140000000	0.250000000	0.44000000	0.4800000	0.5100000	0.5900000	0.7300000	0.9100000	1.0380000	1.0700000
DIFF_LDH	ALIVE	38	0	-9.547368421	95.2511410	15.45177545	52.2500000	-2.30390086	5.50679173	-333.0000000	-318.5700000	-274.4500000	-70.5000000	-23.20000000	-14.50000000	-11.30000000	0.400000000	8.500000000	17.00000000	29.5000000	37.7500000	43.4000000	67.6000000	86.7500000	98.5600000	103.0000000
DIFF_LDH	DEATH	21	0	9.428571429	170.0248721	37.10246888	41.0000000	-0.38842221	6.26609952	-510.0000000	-436.0000000	-140.0000000	-65.0000000	-23.00000000	-9.00000000	-8.00000000	-4.000000000	5.000000000	5.00000000	18.0000000	32.0000000	34.0000000	170.0000000	194.0000000	416.4000000	472.0000000
DIFF_LIVER_UPTAKE	ALIVE	38	0	0.094736842	0.5093755	0.08263162	0.7125000	-1.03381469	1.86159126	-1.5900000	-1.3162000	-0.7225000	-0.4950000	-0.25800000	-0.21500000	-0.09800000	0.018000000	0.120000000	0.27800000	0.4580000	0.4975000	0.5500000	0.6160000	0.6785000	0.8652000	0.8800000
DIFF_LIVER_UPTAKE	DEATH	21	0	-0.121428571	0.4632201	0.10108291	0.6400000	0.13873995	-0.73626526	-0.8700000	-0.8640000	-0.8400000	-0.7400000	-0.38000000	-0.37000000	-0.35000000	-0.320000000	-0.160000000	-0.03000000	0.1900000	0.2700000	0.3000000	0.5100000	0.5200000	0.7200000	0.7700000
DIFF_PLT	ALIVE	38	0	-26.710526316	121.7067605	19.74344366	87.7500000	0.20395211	2.11887063	-354.0000000	-309.2300000	-229.6000000	-166.2000000	-89.80000000	-77.25000000	-60.90000000	-56.400000000	-18.000000000	-8.00000000	3.6000000	10.5000000	19.2000000	96.5000000	180.8000000	288.6200000	316.0000000
DIFF_PLT	DEATH	21	0	11.047619048	141.2085253	30.81422648	58.0000000	1.31681596	5.07388455	-258.0000000	-244.2000000	-189.0000000	-151.0000000	-40.00000000	-24.00000000	-12.00000000	-9.000000000	4.000000000	16.00000000	31.0000000	34.0000000	44.0000000	79.0000000	197.0000000	409.8000000	463.0000000
DIFF_RBC	ALIVE	38	0	-0.280000000	0.7164439	0.11622255	0.9000000	-0.04813573	-0.38757697	-1.9300000	-1.7006000	-1.3015000	-1.2480000	-0.88400000	-0.76750000	-0.67600000	-0.452000000	-0.260000000	0.03200000	0.0970000	0.1325000	0.1640000	0.8020000	0.8475000	1.0538000	1.1500000
DIFF_RBC	DEATH	21	0	-0.367142857	0.6828627	0.14901285	0.6500000	-0.40499884	0.30518589	-1.9700000	-1.8640000	-1.4400000	-1.1100000	-0.86000000	-0.77000000	-0.52000000	-0.410000000	-0.290000000	-0.28000000	-0.1900000	-0.1200000	0.1300000	0.5900000	0.6300000	0.6940000	0.7100000
DIFF_SLR	ALIVE	38	0	0.012379763	0.1893314	0.03071361	0.2242893	0.39905069	0.35975745	-0.3187647	-0.3172185	-0.2956006	-0.2349369	-0.15802676	-0.11211673	-0.09112568	-0.029369579	0.015962444	0.05086431	0.0870160	0.1121725	0.1251329	0.2291428	0.3278227	0.4737786	0.5090979
DIFF_SLR	DEATH	21	0	-0.014920417	0.2696536	0.05884323	0.2990705	0.40533483	1.41268415	-0.5135982	-0.5113946	-0.5025806	-0.2566667	-0.19362704	-0.13682278	-0.13516976	-0.101647059	-0.045342594	0.03066528	0.1359975	0.1622477	0.2076389	0.2268707	0.2537146	0.6017427	0.6887497
DIFF_SPLEEN_UPTAKE	ALIVE	38	0	0.042105263	0.3802855	0.06169046	0.3600000	0.11739142	1.12480748	-0.8000000	-0.7482000	-0.6345000	-0.4380000	-0.18400000	-0.12000000	-0.07900000	-0.010000000	0.055000000	0.15800000	0.2180000	0.2400000	0.2840000	0.4180000	0.6175000	0.9561000	1.1300000
DIFF_SPLEEN_UPTAKE	DEATH	21	0	0.066190476	0.3390940	0.07399638	0.3500000	1.30365796	3.95311040	-0.4200000	-0.4180000	-0.4100000	-0.4000000	-0.14000000	-0.13000000	-0.07000000	-0.050000000	0.050000000	0.10000000	0.2000000	0.2200000	0.2700000	0.3400000	0.3400000	0.9720000	1.1300000
DIFF_WBC	ALIVE	38	0	-0.468421053	2.6410159	0.42842935	3.9525000	0.27158719	-0.72429442	-5.7000000	-5.3596000	-3.9215000	-3.1980000	-2.67600000	-2.42750000	-2.14500000	-1.656000000	-0.855000000	-0.16000000	1.1160000	1.5250000	1.8840000	3.5310000	3.9410000	4.4127000	4.5200000
DIFF_WBC	DEATH	21	0	0.355238095	2.8624982	0.62464833	3.3500000	0.85520225	1.17189325	-4.6400000	-4.2480000	-2.6800000	-2.6700000	-1.94000000	-1.29000000	-1.23000000	-0.360000000	0.100000000	0.38000000	1.1300000	2.0600000	2.3700000	3.2200000	5.4800000	7.2880000	7.7400000
OVERALL_TIME	ALIVE	38	0	17.763157895	16.2537883	2.63671263	16.5000000	1.49609275	1.96731456	1.0000000	1.0000000	1.8500000	2.7000000	4.40000000	6.00000000	7.20000000	11.000000000	14.500000000	15.20000000	20.0000000	22.5000000	25.8000000	41.0000000	51.9500000	64.2600000	65.0000000
OVERALL_TIME	DEATH	21	0	21.523809524	15.1480000	3.30556461	17.0000000	1.30493144	1.91970514	1.0000000	1.6000000	4.0000000	8.0000000	12.00000000	12.00000000	13.00000000	16.000000000	17.000000000	21.00000000	23.0000000	29.0000000	30.0000000	42.0000000	47.0000000	60.6000000	64.0000000
TIME_BETWEE_PET	ALIVE	38	0	10.578947368	8.9703162	1.45517743	11.5000000	1.40520467	1.58539181	1.0000000	1.3700000	2.0000000	2.7000000	4.00000000	4.00000000	5.00000000	6.000000000	7.500000000	9.00000000	11.8000000	15.5000000	18.2000000	22.2000000	26.2000000	36.1500000	38.0000000
TIME_BETWEE_PET	DEATH	21	0	7.904761905	3.9484777	0.86162847	7.0000000	-0.18573481	-0.95771678	1.0000000	1.2000000	2.0000000	3.0000000	4.00000000	4.00000000	5.00000000	8.000000000	9.000000000	9.00000000	10.0000000	11.0000000	11.0000000	12.0000000	13.0000000	14.6000000	15.0000000

Diagrama de dispersion por pareja

# Diagrama de dispersion por pareja

options(repr.plot.width=8, repr.plot.height=5)
var_respuesta <- as.name(respuesta)

for (i in 1:length(var_num)) {
  name_i <- as.name(var_num[i])
  r <- list()
  
    r[[1]] <- ggplot(datos, aes(x=!!name_i, y=!!var_respuesta)) +
      geom_point() +
      geom_smooth(method=lm ,formula = y ~ x, color="red", fill="#69b3a2", se=TRUE)  +
    theme_minimal()
  
  grid.arrange(r[[1]],
               nrow=1, ncol = 1,
               top = textGrob(var_num[i],gp=gpar(fontsize=16,font=3)))
}

Histograma de densidad + boxplot + diagrama de error + violin plot de variables numericas vs respuesta

options(repr.plot.width=16, repr.plot.height=8)

# boxplot + diagrama de error + histograma de densidad + violin plot
for (i in 1:length(var_num)) {
  name_i <- as.name(var_num[i])
  name_y <- as.name(respuesta[1])
  
  r <- list()
  
  # Histograma de densidad
  r[[1]]<-ggplot(datos, aes(x=!!name_i, fill=!!name_y)) +
    geom_density(alpha=0.4)+
    theme_light() +
    #theme(legend.position = "none") +
    xlab("") +
    ylab("Densidad de Frecuencia")
  
  # Boxplot
  r[[2]]<-ggplot(data=datos, aes(y=!!name_i, x=!!name_y)) +
    geom_boxplot(size = 0.4) +
    theme_light() +
    theme(legend.position = "none")+
    xlab("")
  
  # Violin plot con diagramas de error
  r[[3]]<-ggerrorplot(data = datos, x = respuesta, y = var_num[i], 
                      desc_stat = "mean_ci",
                      error.plot = "errorbar",
                      add = c("violin","mean"))+
    theme_light() +
    theme(legend.position = "none") +
    xlab("") +
    ylab("") +
    #ggtitle("Comparacion pvut") +
    stat_compare_means(comparisons = c("0","1"))+ 
    stat_compare_means(label.y = 1.05*max(datos[,var_num[i]]))                 
  
  grid.arrange(r[[1]], r[[2]], r[[3]],
               nrow=3, ncol = 1,
               top = textGrob(var_num[i],gp=gpar(fontsize=16,font=3)))
}

Variables cualitativas vs salida

Graficos de barras de todas las variables cualitativas vs respuesta

name_y <- as.name(respuesta[1])

datos %>%
  group_by(!!name_y) %>%
  plot_bar_category()

Grafico de barras de los efectos secundarios por tratamiento

# EFECTOS SECUNDARIOS POR TRATAMIENTO(podemos hacer todas las combinaciones posibles)
var_cual_x = "CTCNCI"
var_cual_y = "TREATMENT"

name_x = as.name(var_cual_x)
name_y = as.name(var_cual_y)

ggbarstats(datos, x = !!name_x, y = !!name_y)

Grafico apilado de accion del medico por tratamiento

# ACCION DEL MEDICO POR TRATAMIENTO
var_cual_x = "TREATMENT"
var_cual_y = "ACTION_TAKEN_"

name_x = as.name(var_cual_x)
name_y = as.name(var_cual_y)


ggbarstats(datos, x = !!name_x, y = !!name_y)

Grafico comparativo de los efectos adversos con el tratamiento,junto a las acciones tomadas y ciclos entre Pet1 y Pet2

# Grafico comparativo de : efectos adversos con el tratamiento,junto a las acciones tomadas y el ciclo 
ggplot(datos, aes(x =ACTION_TAKEN_, y = CYCLES_BETWEEN_PET1_PET2))+
  geom_bar(
    aes(fill =CTCNCI ), stat = "identity", color = "white",
    position = position_dodge(0.9)
    )+
  facet_wrap(~TREATMENT) + 
  fill_palette("jco")

Grafico comparativo de los pacientes con efectos adversos y la accion del medico segun el tratamiento

# GRAFICO COMPARATIVO DE PACIENTES CON EFECTOS ADVERSOS SEGUN EL TRATAMIENTO Y ACCION DEL MEDICO
ggplot(datos) +
  geom_bar(aes(x=ACTION_TAKEN_, fill=CTCNCI),
           position = "dodge") +
  facet_wrap(~TREATMENT)

Histograma de densidad de DIFF_LIVER_UPTAKE vs tratamiento

# Histograma de densidad de DIFF_LIVER_UPTAKE por tratamiento:
var_x = 'DIFF_LIVER_UPTAKE'
name_x = as.name(var_x)
var_grupo = 'TREATMENT'
name_grupo = as.name(var_grupo)

options(warn=-1)
# With transparency (right)
p2 <- ggplot(data=datos, aes(x=!!name_x, group=!!name_grupo, fill=!!name_grupo)) +
    geom_density(adjust=1.5, alpha=.4) +
    theme_ipsum()
p2

ggplotly(p2)

options(warn=0)

Mosaic plot + Chi cuadrado de las variables cualitativas vs respuesta

# Mosaic plot y chi cuadrado de las variables cualitativas segun la variable respuesta:
r <- list()
for (i in 1:length(var_cual)) {
    # Variable de salida o respuesta
    name_y <- as.name(respuesta[1])

    # Variable de entrada
    name_i <- as.name(var_cual[i])


    # Tabla de contingencias de una variable con la target
    categ <- target_by(datos, !!name_y)
    cat_cat <- relate(categ, !!name_i)
    r[[i]]<-as.data.frame.matrix(cat_cat)
    
    # Chi cuadrado
    print(summary(cat_cat))
    
    # Mosaico de una variable con la target
    grid.arrange(plot(cat_cat),nrow=1, ncol = 1)
    
}
r

Call: xtabs(formula = formula_str, data = data, addNA = TRUE)
Number of cases in table: 59 
Number of factors: 2 
Test for independence of all factors:
	Chisq = 1.4811, df = 1, p-value = 0.2236
Call: xtabs(formula = formula_str, data = data, addNA = TRUE)
Number of cases in table: 59 
Number of factors: 2 
Test for independence of all factors:
	Chisq = 9.495, df = 3, p-value = 0.02338
	Chi-squared approximation may be incorrect

Call: xtabs(formula = formula_str, data = data, addNA = TRUE)
Number of cases in table: 59 
Number of factors: 2 
Test for independence of all factors:
	Chisq = 12.737, df = 9, p-value = 0.1749
	Chi-squared approximation may be incorrect

Call: xtabs(formula = formula_str, data = data, addNA = TRUE)
Number of cases in table: 59 
Number of factors: 2 
Test for independence of all factors:
	Chisq = 0.009083, df = 1, p-value = 0.9241

Call: xtabs(formula = formula_str, data = data, addNA = TRUE)
Number of cases in table: 59 
Number of factors: 2 
Test for independence of all factors:
	Chisq = 5.748, df = 4, p-value = 0.2188
	Chi-squared approximation may be incorrect

Call: xtabs(formula = formula_str, data = data, addNA = TRUE)
Number of cases in table: 59 
Number of factors: 2 
Test for independence of all factors:
	Chisq = 5.938, df = 5, p-value = 0.3123
	Chi-squared approximation may be incorrect

Call: xtabs(formula = formula_str, data = data, addNA = TRUE)
Number of cases in table: 59 
Number of factors: 2 
Test for independence of all factors:
	Chisq = 2.7546, df = 4, p-value = 0.5997
	Chi-squared approximation may be incorrect

Call: xtabs(formula = formula_str, data = data, addNA = TRUE)
Number of cases in table: 59 
Number of factors: 2 
Test for independence of all factors:
	Chisq = 13.937, df = 6, p-value = 0.03035
	Chi-squared approximation may be incorrect

1. A data.frame: 2 × 2

   	FEMALE	MALE
   	<int>	<int>
   ALIVE	15	23
   DEATH	5	16

2. A data.frame: 2 × 4

   	I	II	III	IV
   	<int>	<int>	<int>	<int>
   ALIVE	1	8	19	10
   DEATH	5	7	7	2

3. A data.frame: 2 × 10

   	EWING SARCOMA	GASTRIC CANCER	GINECOLOGICAL	HEAD AND NECK	LUNG CANCER	MELANOMA	PANCREAS CANCER	RENAL CANCER	SARCOMA	UROTHELIAL CARCINOMA
   	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>
   ALIVE	2	0	6	6	14	2	4	3	0	1
   DEATH	0	3	1	4	5	3	3	1	1	0

4. A data.frame: 2 × 2

   	CHEMO	ICI
   	<int>	<int>
   ALIVE	24	14
   DEATH	13	8

5. A data.frame: 2 × 5

   	ASYMTOMATIC	BEDBOUND	SYMPTOMATIC >50 % IN THE BED	SYMPTOMATIC BUT AMBULATORY	SYMPTOMATIC,<50% IN BED DURING THE DAY
   	<int>	<int>	<int>	<int>	<int>
   ALIVE	19	0	1	8	10
   DEATH	7	1	0	9	4

6. A data.frame: 2 × 6

   	ATEROESCLEROSIS	CANCER RRECURRENCE	CHRONIC INFLAMMATION	DIABETES MELLITUS	HYPERLIPIDEMIA	HYPERTENSION
   	<int>	<int>	<int>	<int>	<int>	<int>
   ALIVE	2	3	5	9	7	12
   DEATH	1	5	4	4	5	2

7. A data.frame: 2 × 5

   	HIGHT SEVERE	MODERATE	NO SIDE EFFECTS	SEVERE	SLIGTHLY SIDE EFFCTS
   	<int>	<int>	<int>	<int>	<int>
   ALIVE	2	6	21	7	2
   DEATH	2	5	7	5	2

8. A data.frame: 2 × 7

   	ADDED OTHER TREATMEN	ADDED STEROIDS	DOSE NOT CHANGED	DOSE REDUCED	DRUG INTERRUPTED	DRUG WIHDRAWN	UNKNOW
   	<int>	<int>	<int>	<int>	<int>	<int>	<int>
   ALIVE	3	0	17	9	3	0	6
   DEATH	0	1	4	8	6	1	1

DEFINIMOS LOS DATAFRAMES DE ENTRADA Y SALIDA

# Voy a definir el data frame de entrada: X
X = datos[-ncol(datos)]
head(X)

# Voy a definir la variable de salida:
y = as.factor(as.vector(unlist(datos[,respuesta])))
head(y)

A tibble: 6 × 33

STATUS	CYCLES_BETWEEN_PET1_PET2	GENDER	AGE	TNM_STAGE	DIFF_WBC	DIFF_RBC	DIFF_HB	DIFF_PLT	DIFF_CRP	DIFF_ALBUMIN	DIFF_LDH	DIFF_eGFR	DIFF_AST	DIFF_ALT	DIFF_K	DIFF_BGL	BMI	DIFF_BW	DIFF_SPLEEN_UPTAKE	DIFF_BM_UPTAKE	DIFF_LIVER_UPTAKE	DIFF_ESTIMATED_SPLEEN_VOL	DIAGNOSTIC	TREATMENT	ECOGPS	COMORBIDITIES	CTCNCI	ACTION_TAKEN_	TIME_BETWEE_PET	dias	DIFF_SLR	DIFF_BMLR
<fct>	<dbl>	<fct>	<dbl>	<fct>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<fct>	<fct>	<fct>	<fct>	<fct>	<fct>	<dbl>	<dbl>	<dbl>	<dbl>
ALIVE	3	MALE	54	III	-1.83	-0.64	-2.20	-82	-0.56	0.98	-271.0	23.7	0.3	13.2	0.10	12	22.90	-4.0	0.02	0.12	0.44	-80.9	GINECOLOGICAL	ICI	SYMPTOMATIC BUT AMBULATORY	HYPERLIPIDEMIA	SEVERE	DRUG INTERRUPTED	2	25	0.029335236	0.10905937
ALIVE	2	FEMALE	79	IV	-3.08	0.14	0.70	-159	-3.80	1.33	22.0	-0.2	4.4	5.3	0.58	-14	16.47	0.3	-0.20	-0.60	-0.30	6.2	HEAD AND NECK	ICI	ASYMTOMATIC	HYPERTENSION	NO SIDE EFFECTS	DRUG INTERRUPTED	4	0	-0.156566790	-0.04688961
ALIVE	3	MALE	60	III	-3.18	-0.50	3.54	22	-0.06	-0.20	64.2	4.6	-2.6	1.1	0.33	12	20.15	-8.1	-0.12	0.34	0.27	9.1	LUNG CANCER	ICI	SYMPTOMATIC,<50% IN BED DURING THE DAY	DIABETES MELLITUS	MODERATE	DOSE REDUCED	4	14	-0.044698028	-0.36568856
ALIVE	3	MALE	76	II	-1.28	0.79	1.70	9	0.04	-0.12	-55.0	-8.9	-0.7	-6.7	0.05	-4	22.83	0.0	0.46	-0.40	0.59	-38.8	RENAL CANCER	ICI	ASYMTOMATIC	HYPERTENSION	NO SIDE EFFECTS	DOSE NOT CHANGED	6	0	-0.002886671	0.13061297
ALIVE	5	FEMALE	70	II	0.00	-0.09	-0.40	-95	-7.23	0.05	6.0	-13.2	2.7	2.2	0.06	-8	18.29	-0.1	-0.34	-0.80	-0.48	-87.6	MELANOMA	ICI	SYMPTOMATIC BUT AMBULATORY	DIABETES MELLITUS	NO SIDE EFFECTS	DOSE NOT CHANGED	6	0	-0.095215760	-0.25268025
ALIVE	4	MALE	54	IV	0.99	0.10	0.00	-3	0.02	-0.16	-4.0	-13.1	0.6	2.8	-0.47	-3	27.18	-10.8	0.00	-0.10	-0.53	34.4	LUNG CANCER	ICI	ASYMTOMATIC	CANCER RRECURRENCE	HIGHT SEVERE	DOSE NOT CHANGED	6	363	-0.167591661	0.07251379

1. ALIVE
2. ALIVE
3. ALIVE
4. ALIVE
5. ALIVE
6. ALIVE

Levels:

1. 'ALIVE'
2. 'DEATH'

FEATURE SELECTION (SELECCION DE CARACTERISTICAS)

1. Feature selection univariada ( ANOVA/Kruskall Wallis = Cuantitativa vs salida o Chi Cuadrado = Cualitativas vs salida, MI = Mutual information)

• Quitar las variables con MI = 0
• Quitar las variables con p-valores >25% = 0.25

1. Feature selection multivariado:

• RFE - recursive feature elimination RANDOM FOREST( para pocos datos como este dataset lo probe y no funciona bien)
• Modelo logístico (caso binario) mejor modelo según el BIC. --> con pocos datos y pocas variables(para este caso seleccionando las variables importantes)

Feature selection univariado: cuantitativas vs la salida

Wilcoxon rank test

#options(warn=-1)
## Multiple Wilcoxon rank sum tests - casos binarios
datos$STATUS <- as.numeric(datos$STATUS)
Wilk_p <- as.data.frame(t(as.data.frame(lapply(datos[,var_num], function(x) wilcox.test(x ~ datos$STATUS)$p.value)))) %>%
  arrange(V1)
         
Wilk_p  
#options(warn=-0)

A data.frame: 25 × 1

	V1
	<dbl>
DIFF_BW	0.001071841
DIFF_LIVER_UPTAKE	0.052455399
dias	0.108429874
DIFF_PLT	0.115177643
DIFF_CRP	0.151877257
OVERALL_TIME	0.168110480
BMI	0.205329235
DIFF_HB	0.225794623
DIFF_K	0.244513380
CYCLES_BETWEEN_PET1_PET2	0.250401922
DIFF_WBC	0.271201503
AGE	0.366288338
DIFF_BMLR	0.466463400
DIFF_AST	0.536942262
DIFF_BGL	0.557948763
DIFF_SLR	0.669052785
DIFF_RBC	0.680615800
TIME_BETWEE_PET	0.744938990
DIFF_ALBUMIN	0.769606349
DIFF_ESTIMATED_SPLEEN_VOL	0.771492426
DIFF_LDH	0.806140116
DIFF_eGFR	0.924323941
DIFF_SPLEEN_UPTAKE	0.974737237
DIFF_BM_UPTAKE	0.987368328
DIFF_ALT	1.000000000

Kruskall Wallis test

## Kruskall wallis rank sum tests - para todos los casos
datos$STATUS <- as.numeric(datos$STATUS)
Kruskal_p <- as.data.frame(t(as.data.frame(lapply(datos[,var_num], function(x) kruskal.test(x ~ datos$STATUS)$p.value)))) %>%
  arrange(V1)
                                               
Kruskal_p

A data.frame: 25 × 1

	V1
	<dbl>
DIFF_BW	0.001042032
DIFF_LIVER_UPTAKE	0.051499589
dias	0.106574249
DIFF_PLT	0.113362723
DIFF_CRP	0.149567892
OVERALL_TIME	0.165679149
BMI	0.202511282
DIFF_HB	0.222742163
DIFF_K	0.241312102
CYCLES_BETWEEN_PET1_PET2	0.247127164
DIFF_WBC	0.267761696
AGE	0.362099048
DIFF_BMLR	0.461632677
DIFF_AST	0.531723207
DIFF_BGL	0.552640237
DIFF_SLR	0.663298181
DIFF_RBC	0.674817935
TIME_BETWEE_PET	0.738942177
DIFF_ALBUMIN	0.763549184
DIFF_ESTIMATED_SPLEEN_VOL	0.763569297
DIFF_LDH	0.800017121
DIFF_eGFR	0.918037782
DIFF_SPLEEN_UPTAKE	0.968420824
DIFF_BM_UPTAKE	0.981053204
DIFF_ALT	0.993683966

Anova

Ahora hacemos la ANOVA que es el equivalente paramétrico a Kurskall Wallis

## ANOVA - para todos los casos
datos$STATUS <- as.factor(datos$STATUS)
ANOVA_p<-as.data.frame(t(as.data.frame(lapply(datos[,var_num], function(x) anova(lm(x ~ datos$STATUS))$`Pr(>F)` [1])))) %>%
  arrange(V1)
                                              
ANOVA_p

A data.frame: 25 × 1

	V1
	<dbl>
DIFF_BW	0.0008674499
DIFF_LIVER_UPTAKE	0.1128440197
BMI	0.1313002214
DIFF_HB	0.1987145533
TIME_BETWEE_PET	0.2006455075
DIFF_ALT	0.2283988495
DIFF_WBC	0.2702341457
DIFF_PLT	0.2858364966
DIFF_K	0.3178280254
AGE	0.3199523200
OVERALL_TIME	0.3872791867
DIFF_BMLR	0.4251613367
dias	0.4311242737
DIFF_AST	0.4411739211
DIFF_CRP	0.5579829726
DIFF_BGL	0.5739834861
DIFF_LDH	0.5836796913
DIFF_eGFR	0.5854167472
DIFF_RBC	0.6510598009
DIFF_SLR	0.6511375206
CYCLES_BETWEEN_PET1_PET2	0.6804891959
DIFF_ESTIMATED_SPLEEN_VOL	0.7346993354
DIFF_SPLEEN_UPTAKE	0.8098195294
DIFF_BM_UPTAKE	0.9039677773
DIFF_ALBUMIN	0.9999543656

Feature selection univariado: cualitativa vs la salida

datos$STATUS<-as.numeric(datos$STATUS)
datos$GENDER <- as.numeric(datos$GENDER)
datos$COMORBIDITIES <- as.numeric(datos$COMORBIDITIES)
datos$CTCNCI <- as.numeric(datos$CTCNCI)
datos$ACTION_TAKEN_ <- as.numeric(datos$ACTION_TAKEN_)
datos$TNM_STAGE <- as.numeric(datos$TNM_STAGE)
datos$DIAGNOSTIC <- as.numeric(datos$DIAGNOSTIC)
datos$TREATMENT <- as.numeric(datos$TREATMENT)
datos$ECOGPS <- as.numeric(datos$ECOGPS)

Chi cuadrado de Pearson

res_sig <- rep(0,length(var_cual))

for (i in 1:length(var_cual)){
  chisq <- chisq.test(table(as.factor(as.character(datos[,var_cual[i]])),datos[,'STATUS']),correct = TRUE,simulate.p.value = 10000)
  res_sig[i]<-chisq$p.value
}
df_sig <- data.frame(p_valor=res_sig)
row.names(df_sig) <- colnames(datos[,var_cual])

idx_sig <- sort(df_sig$p_valor,decreasing = FALSE,index.return=TRUE)$ix
df_sig_sort <- data.frame(p_valor=res_sig[idx_sig])
row.names(df_sig_sort) <- colnames(datos[,var_cual])[idx_sig]
df_sig_sort

A data.frame: 8 × 1

	p_valor
	<dbl>
ACTION_TAKEN_	0.01399300
TNM_STAGE	0.02348826
DIAGNOSTIC	0.13593203
ECOGPS	0.18440780
GENDER	0.25187406
COMORBIDITIES	0.31534233
CTCNCI	0.67266367
TREATMENT	1.00000000

Filtro univariado Mutual information (MI) - TODOS los casos

# Mutual informacion variables cuantitativas:
inf_gain <- information.gain(STATUS~., data = datos)
inf_gain <- inf_gain %>%
  arrange(desc(attr_importance))
inf_gain

A data.frame: 33 × 1

	attr_importance
	<dbl>
TREATMENT	0.4393713
DIFF_BW	0.2900315
CYCLES_BETWEEN_PET1_PET2	0.0000000
GENDER	0.0000000
AGE	0.0000000
TNM_STAGE	0.0000000
DIFF_WBC	0.0000000
DIFF_RBC	0.0000000
DIFF_HB	0.0000000
DIFF_PLT	0.0000000
DIFF_CRP	0.0000000
DIFF_ALBUMIN	0.0000000
DIFF_LDH	0.0000000
DIFF_eGFR	0.0000000
DIFF_AST	0.0000000
DIFF_ALT	0.0000000
DIFF_K	0.0000000
DIFF_BGL	0.0000000
BMI	0.0000000
DIFF_SPLEEN_UPTAKE	0.0000000
DIFF_BM_UPTAKE	0.0000000
DIFF_LIVER_UPTAKE	0.0000000
DIFF_ESTIMATED_SPLEEN_VOL	0.0000000
DIAGNOSTIC	0.0000000
ECOGPS	0.0000000
COMORBIDITIES	0.0000000
CTCNCI	0.0000000
ACTION_TAKEN_	0.0000000
TIME_BETWEE_PET	0.0000000
dias	0.0000000
DIFF_SLR	0.0000000
DIFF_BMLR	0.0000000
OVERALL_TIME	0.0000000

Feature selection multivariado - BIC modelo estadístico - BINARIO

row.names(df_sig_sort)[1:3]

1. 'ACTION_TAKEN_'
2. 'TNM_STAGE'
3. 'DIAGNOSTIC'

row.names(Kruskal_p)[1:5]

1. 'DIFF_BW'
2. 'DIFF_LIVER_UPTAKE'
3. 'dias'
4. 'DIFF_PLT'
5. 'DIFF_CRP'

datos$STATUS<- as.factor(datos$STATUS)

modelos_5top <-
  glmulti(STATUS~ACTION_TAKEN_+TNM_STAGE+DIAGNOSTIC+DIFF_BW+DIFF_LIVER_UPTAKE+dias+DIFF_PLT+DIFF_CRP, data = datos,
          level = 1,               # No interaction considered
          method = "h",            # Exhaustive approach
          crit = "bic",            # BIC as criteria
          confsetsize = 5,         # Keep 5 best models
          plotty = F, report = F,  # No plot or interim reports
          fitfunction = "glm",     # glm function
          family = binomial)       # binomial family for logistic regression

# Los 5 mejores modelos
print(modelos_5top@formulas)
options(warn = -1)

[[1]]
STATUS ~ 1 + TNM_STAGE + DIFF_BW
<environment: 0x000002432f554a78>

[[2]]
STATUS ~ 1 + DIFF_BW
<environment: 0x000002432f554a78>

[[3]]
STATUS ~ 1 + TNM_STAGE + DIFF_BW + DIFF_LIVER_UPTAKE
<environment: 0x000002432f554a78>

[[4]]
STATUS ~ 1 + TNM_STAGE + DIFF_BW + dias
<environment: 0x000002432f554a78>

[[5]]
STATUS ~ 1 + TNM_STAGE + DIFF_BW + DIFF_PLT
<environment: 0x000002432f554a78>

# El summary de los 5 mejores modelos
summary(modelos_5top@objects[[3]])

Call:
fitfunc(formula = as.formula(x), family = ..1, data = data)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9267  -0.6980  -0.4480   0.8274   1.9488  

Coefficients:
                  Estimate Std. Error z value Pr(>|z|)  
(Intercept)        1.23509    1.02726   1.202   0.2292  
TNM_STAGE         -0.83662    0.37196  -2.249   0.0245 *
DIFF_BW            0.11358    0.04968   2.286   0.0222 *
DIFF_LIVER_UPTAKE -0.86902    0.62713  -1.386   0.1658  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 76.823  on 58  degrees of freedom
Residual deviance: 57.697  on 55  degrees of freedom
AIC: 65.697

Number of Fisher Scoring iterations: 5

# Las variables que aparecen en el mejor modelo son:
var_sel_mod1<-names(modelos_5top@objects[[3]]$coefficients)[-1]
print(var_sel_mod1)

[1] "TNM_STAGE"         "DIFF_BW"           "DIFF_LIVER_UPTAKE"

var_sel_mod1 <- c("TNM_STAGE","DIFF_BW","DIFF_LIVER_UPTAKE")
var_sel_mod1

1. 'TNM_STAGE'
2. 'DIFF_BW'
3. 'DIFF_LIVER_UPTAKE'

# Calculamos el mejor modelo y su accuracy:

# Construimos la formula de forma automatica:
x = paste(var_sel_mod1, collapse = " + ")
y = respuesta
formBIC = as.formula(paste(y, "~", x))
formBIC

STATUS ~ TNM_STAGE + DIFF_BW + DIFF_LIVER_UPTAKE

# Hacemos la matriz de confusion y los estadisticos del modelo
modelo1 <- train(formBIC, data=datos,method='glm')

    print("Matriz de confusión")
    pred <- predict(newdata=datos,modelo1) # prediccion usando el modelo logistico
    real <- as.factor(datos[,respuesta]) # respuesta real
    cm_train_TOT <- caret::confusionMatrix(data=pred,reference=real) # guardamos la matriz de confusion de training
    print(cm_train_TOT) # pintamos la matriz de confusion de training

[1] "Matriz de confusión"
Confusion Matrix and Statistics

          Reference
Prediction  1  2
         1 33 12
         2  5  9
                                          
               Accuracy : 0.7119          
                 95% CI : (0.5792, 0.8224)
    No Information Rate : 0.6441          
    P-Value [Acc > NIR] : 0.1709          
                                          
                  Kappa : 0.3209          
                                          
 Mcnemar's Test P-Value : 0.1456          
                                          
            Sensitivity : 0.8684          
            Specificity : 0.4286          
         Pos Pred Value : 0.7333          
         Neg Pred Value : 0.6429          
             Prevalence : 0.6441          
         Detection Rate : 0.5593          
   Detection Prevalence : 0.7627          
      Balanced Accuracy : 0.6485          
                                          
       'Positive' Class : 1               
                                          

Feature selection Boruta

Una de las técnicas más completas que podemos aplicar es la metodología de Boruta.El problema es que el dataset tiene pocas observaciones y para que funcione perfectamente es mejor usar mas,pero lo aplicamos como comparacion.

# Voy a definir el data frame de entrada: X
X = datos[-1]
head(X)

# Voy a definir la variable de salida:
y = as.factor(as.vector(unlist(datos[,respuesta])))
head(y)

A tibble: 6 × 33

CYCLES_BETWEEN_PET1_PET2	GENDER	AGE	TNM_STAGE	DIFF_WBC	DIFF_RBC	DIFF_HB	DIFF_PLT	DIFF_CRP	DIFF_ALBUMIN	DIFF_LDH	DIFF_eGFR	DIFF_AST	DIFF_ALT	DIFF_K	DIFF_BGL	BMI	DIFF_BW	DIFF_SPLEEN_UPTAKE	DIFF_BM_UPTAKE	DIFF_LIVER_UPTAKE	DIFF_ESTIMATED_SPLEEN_VOL	DIAGNOSTIC	TREATMENT	ECOGPS	COMORBIDITIES	CTCNCI	ACTION_TAKEN_	TIME_BETWEE_PET	dias	DIFF_SLR	DIFF_BMLR	OVERALL_TIME
<dbl>	<fct>	<dbl>	<fct>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<fct>	<fct>	<fct>	<fct>	<fct>	<fct>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
3	MALE	54	III	-1.83	-0.64	-2.20	-82	-0.56	0.98	-271.0	23.7	0.3	13.2	0.10	12	22.90	-4.0	0.02	0.12	0.44	-80.9	GINECOLOGICAL	ICI	SYMPTOMATIC BUT AMBULATORY	HYPERLIPIDEMIA	SEVERE	DRUG INTERRUPTED	2	25	0.029335236	0.10905937	2
2	FEMALE	79	IV	-3.08	0.14	0.70	-159	-3.80	1.33	22.0	-0.2	4.4	5.3	0.58	-14	16.47	0.3	-0.20	-0.60	-0.30	6.2	HEAD AND NECK	ICI	ASYMTOMATIC	HYPERTENSION	NO SIDE EFFECTS	DRUG INTERRUPTED	4	0	-0.156566790	-0.04688961	50
3	MALE	60	III	-3.18	-0.50	3.54	22	-0.06	-0.20	64.2	4.6	-2.6	1.1	0.33	12	20.15	-8.1	-0.12	0.34	0.27	9.1	LUNG CANCER	ICI	SYMPTOMATIC,<50% IN BED DURING THE DAY	DIABETES MELLITUS	MODERATE	DOSE REDUCED	4	14	-0.044698028	-0.36568856	2
3	MALE	76	II	-1.28	0.79	1.70	9	0.04	-0.12	-55.0	-8.9	-0.7	-6.7	0.05	-4	22.83	0.0	0.46	-0.40	0.59	-38.8	RENAL CANCER	ICI	ASYMTOMATIC	HYPERTENSION	NO SIDE EFFECTS	DOSE NOT CHANGED	6	0	-0.002886671	0.13061297	12
5	FEMALE	70	II	0.00	-0.09	-0.40	-95	-7.23	0.05	6.0	-13.2	2.7	2.2	0.06	-8	18.29	-0.1	-0.34	-0.80	-0.48	-87.6	MELANOMA	ICI	SYMPTOMATIC BUT AMBULATORY	DIABETES MELLITUS	NO SIDE EFFECTS	DOSE NOT CHANGED	6	0	-0.095215760	-0.25268025	12
4	MALE	54	IV	0.99	0.10	0.00	-3	0.02	-0.16	-4.0	-13.1	0.6	2.8	-0.47	-3	27.18	-10.8	0.00	-0.10	-0.53	34.4	LUNG CANCER	ICI	ASYMTOMATIC	CANCER RRECURRENCE	HIGHT SEVERE	DOSE NOT CHANGED	6	363	-0.167591661	0.07251379	18

1. 1
2. 1
3. 1
4. 1
5. 1
6. 1

Levels:

1. '1'
2. '2'

# Determinar la importancia de los atributos.
boruta.model <- Boruta(y~., data = cbind(X,y), doTrace = 2)

 1. run of importance source...

 2. run of importance source...

 3. run of importance source...

 4. run of importance source...

 5. run of importance source...

 6. run of importance source...

 7. run of importance source...

 8. run of importance source...

 9. run of importance source...

 10. run of importance source...

 11. run of importance source...

 12. run of importance source...

After 12 iterations, +0.53 secs: 

 rejected 30 attributes: ACTION_TAKEN_, AGE, BMI, COMORBIDITIES, CTCNCI and 25 more;

 still have 3 attributes left.


 13. run of importance source...

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After 20 iterations, +0.79 secs: 

 confirmed 1 attribute: DIFF_BW;

 still have 2 attributes left.


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After 90 iterations, +2.8 secs: 

 confirmed 1 attribute: TNM_STAGE;

 still have 1 attribute left.


 91. run of importance source...

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options(repr.plot.width=12, repr.plot.height=8)

print(boruta.model)
plot(boruta.model)

Boruta performed 99 iterations in 3.069594 secs.
 2 attributes confirmed important: DIFF_BW, TNM_STAGE;
 30 attributes confirmed unimportant: ACTION_TAKEN_, AGE, BMI,
COMORBIDITIES, CTCNCI and 25 more;
 1 tentative attributes left: DIFF_CRP;

# Refinar modelo para resolver posibles atributos tentativos.
# Dado que pueden existir atributos no resueltos (tentativos), refinamos el modelo.

boruta.model2 <- TentativeRoughFix(boruta.model)
print(boruta.model2)
plot(boruta.model2)

Boruta performed 99 iterations in 3.069594 secs.
Tentatives roughfixed over the last 99 iterations.
 2 attributes confirmed important: DIFF_BW, TNM_STAGE;
 31 attributes confirmed unimportant: ACTION_TAKEN_, AGE, BMI,
COMORBIDITIES, CTCNCI and 26 more;

# Obtener una lista de los atributos y sus etiquetas con el analisis de Boruta
as.data.frame(boruta.model$finalDecision)

A data.frame: 33 × 1

	boruta.model$finalDecision
	<fct>
CYCLES_BETWEEN_PET1_PET2	Rejected
GENDER	Rejected
AGE	Rejected
TNM_STAGE	Confirmed
DIFF_WBC	Rejected
DIFF_RBC	Rejected
DIFF_HB	Rejected
DIFF_PLT	Rejected
DIFF_CRP	Tentative
DIFF_ALBUMIN	Rejected
DIFF_LDH	Rejected
DIFF_eGFR	Rejected
DIFF_AST	Rejected
DIFF_ALT	Rejected
DIFF_K	Rejected
DIFF_BGL	Rejected
BMI	Rejected
DIFF_BW	Confirmed
DIFF_SPLEEN_UPTAKE	Rejected
DIFF_BM_UPTAKE	Rejected
DIFF_LIVER_UPTAKE	Rejected
DIFF_ESTIMATED_SPLEEN_VOL	Rejected
DIAGNOSTIC	Rejected
TREATMENT	Rejected
ECOGPS	Rejected
COMORBIDITIES	Rejected
CTCNCI	Rejected
ACTION_TAKEN_	Rejected
TIME_BETWEE_PET	Rejected
dias	Rejected
DIFF_SLR	Rejected
DIFF_BMLR	Rejected
OVERALL_TIME	Rejected

# Obtener una lista de los atributos importantes y tentativos
opc_boruta = getSelectedAttributes(boruta.model2, withTentative = F)
opc_boruta

1. 'TNM_STAGE'
2. 'DIFF_BW'

# Construimos la formula de forma automatica:
x = paste(opc_boruta, collapse = " + ")
y = respuesta
formBoruta = as.formula(paste(y, "~", x))
formBoruta

STATUS ~ TNM_STAGE + DIFF_BW

Curva Roc

library(ROCR)

index = sample(1:nrow(datos), size = .100 * nrow(datos))
#index
train = datos[index, ]
test = datos[-index, ]

model = glm(STATUS~ACTION_TAKEN_+TNM_STAGE+DIAGNOSTIC+DIFF_BW+DIFF_LIVER_UPTAKE+dias+DIFF_PLT+DIFF_CRP,data=train, 
            family = binomial(link = "logit"))

pred = predict(model,test,type="response")
pred = prediction(pred,test$STATUS)
perf = performance(pred, "acc")
#plot(perf)

max_ind = which.max(slot(perf, "y.values")[[1]] )
acc = slot(perf, "y.values")[[1]][max_ind]
cutoff = slot(perf, "x.values")[[1]][max_ind]
print(c(accuracy= acc))

perf_cost = performance(pred, "cost")
perf_err = performance(pred, "err")
perf_tpr = performance(pred, "tpr")
perf_sn_sp = performance(pred, "sens", "spec")

roc = performance(pred,"tpr","fpr")
plot(roc, colorize = T, lwd = 2)
abline(a = 0, b = 1)

auc = performance(pred, measure = "auc")
print(auc@y.values)

 accuracy 
0.7222222 
[[1]]
[1] 0.7022556

CONCLUSIONES

El principal problema que nos hemos encontrado es que es un dataset con pocas observaciones.Aunque la estadistica inferencial procura sacar conclusiones generales a traves de una muestra , esta debe ser lo suficientemente significativa para que los distintos test y modelos puedan hacer su trabajo con un minimo de confiabilidad y minuciosidad.
A pesar de ello hemos sacado algunas conclusiones que quiza no sean importantes o relevantes en la medida de lo que queriamos. Pero por otro lado observando con minuciosidad los distintos graficos comparativos si podemos extraer conclusiones mas valiosas.